Uncertainty tolerance among primary care physicians: Relationship to shared decision making-related perceptions, practices, and physician characteristics

OBJECTIVE

Understand how physicians' uncertainty tolerance (UT) in clinical care relates to their personal characteristics, perceptions and practices regarding shared decision making (SDM).

METHODS

As part of a trial of SDM training about colorectal cancer screening, primary care physicians (n = 67) completed measures of their uncertainty tolerance in medical practice (Anxiety subscale of the Physician's Reactions to Uncertainty Scale, PRUS-A), and their SDM self-efficacy (confidence in SDM skills). Patients (N = 466) completed measures of SDM (SDM Process scale) after a clinical visit. Bivariate regression analyses and multilevel regression analyses examined relationships.

RESULTS

Higher UT was associated with greater physician age (p = .01) and years in practice (p = 0.015), but not sex or race. Higher UT was associated with greater SDM self-efficacy (p < 0.001), but not patient-reported SDM.

CONCLUSION

Greater age and practice experience predict greater physician UT, suggesting that UT might be improved through training, while UT is associated with greater confidence in SDM, suggesting that improving UT might improve SDM. However, UT was unassociated with patient-reported SDM, raising the need for further studies of these relationships.

PRACTICE IMPLICATIONS

Developing and implementing training interventions aimed at increasing physician UT may be a promising way to promote SDM in clinical care.

Universal dynamic fitting of magnetic resonance spectroscopy

PURPOSE

Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type.

METHODS

A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting.

RESULTS

Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data.

CONCLUSION

A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.

Balancing risks and rewards: How hematologists discuss uncertainty in allogeneic hematopoietic cell transplantation outcomes

OBJECTIVE

Allogeneic hematopoietic cell transplant (alloHCT) offers many patients with blood cancers a chance of cure but carries risks. We characterized how hematologists discuss the high-risk, high-reward concept of alloHCT.

METHODS

Qualitative analysis of video-recorded virtual encounters of hematologists who routinely perform alloHCT with actors portraying an older man recently diagnosed with high-risk myelodysplastic syndrome.

RESULTS

Hematologists (n = 37) were a median age of 44 years, 65% male, and 68% white. They frequently used "teeter-totter" language that juxtaposed alloHCT's risks and rewards in a dynamic, quickly alternating fashion and communicated uncertainty in transplant outcomes. This dialogue oscillated between encouragement about alloHCT's potential for cure and caution about its risks and occurred within single speech turns and in exchanges between hematologist and patient. Fewer hematologists outlined their big-picture stance on transplant's risks and benefits early in the conversation. Meanwhile, hematologists varied in how they counseled patients to manage transplant-related uncertainty and consider treatment decision making.

CONCLUSION

Hematologists use "teeter-totter" language to express hope and concern, confidence and uncertainty, and encouragement and caution about the high-risk, high-reward nature of alloHCT.

PRACTICE IMPLICATIONS

Teeter-totter language may help frame big-picture content about alloHCT's risks and benefits that is essential for patient education and decision making.

Is exercise therapy the right treatment for rotator cuff-related shoulder pain? Uncertainties, theory, and practice

BACKGROUND

Exercise therapy is a popular non-surgical treatment to help manage individuals with rotator cuff-related shoulder pain (RCRSP) and is recommended in all clinical practice guidelines. Due to modest effect sizes, low quality evidence, uncertainty relating to efficacy, and mechanism(s) of benefit, exercise as a therapeutic intervention has been the subject of increasing scrutiny.

AIMS

The aim of this critical review is to lay out where the purported uncertainties of exercise for RCRSP exist by exploring the relevant quantitative and qualitative literature. We conclude by offering theoretical and practical considerations to help reduce the uncertainty of delivering exercise therapy in a clinical environment.

RESULTS AND DISCUSSION

Uncertainty underpins much of the theory and practice of delivering exercise therapy for individuals with RCRSP. Nonetheless, exercise is an often-valued treatment by individuals with RCRSP, when provided within an appropriate clinical context. We encourage clinicians to use a shared decision-making paradigm and embrace a pluralistic model when prescribing therapeutic exercise. This may take the form of using exercise experiments to trial different exercise approaches, adjusting, and adapting the exercise type, load, and context based on the individual's symptom irritability, preferences, and goals.

CONCLUSION

We contend that providing exercise therapy should remain a principal treatment option for helping individuals with RCRSP. Limitations notwithstanding, exercise therapy is relatively low cost, accessible, and often valued by individuals with RCRSP. The uncertainty surrounding exercise therapy requires ongoing research and emphasis could be directed towards investigating causal mechanisms to better understand how exercise may benefit an individual with RCRSP.

Disease X epidemic control using a stochastic model and a deterministic approximation: Performance comparison with and without parameter uncertainties

BACKGROUND

The spread of infectious diseases can be modeled using deterministic or stochastic models. A deterministic approximation of a stochastic model can be appropriate under some conditions, but is unable to capture the discrete nature of populations. We look into the choice of a model from the perspective of decision making.

METHOD

We consider an emerging disease (Disease X) in a closed population modeled by a stochastic SIR model or its deterministic approximation. The objective of the decision maker is to minimize the cumulative number of symptomatic infected-days over the course of the epidemic by picking a vaccination policy. We consider four decision making scenarios: based on the stochastic model or the deterministic model, and with or without parameter uncertainty. We also consider different sample sizes for uncertain parameter draws and stochastic model runs. We estimate the average performance of decision making in each scenario and for each sample size.

RESULTS

The model used for decision making has an influence on the picked policies. The best achievable performance is obtained with the stochastic model, knowing parameter values, and for a large sample size. For small sample sizes, the deterministic model can outperform the stochastic model due to stochastic effects. Resolving uncertainties may bring more benefit than switching to the stochastic model in our example.

CONCLUSION

This article illustrates the interplay between the choice of a type of model, parameter uncertainties, and sample sizes. It points to issues to be considered when optimizing a stochastic model.

Urged to feel certain again: The role of emotion-related impulsivity on the relationships between intolerance of uncertainty and OCD symptom severity

OBJECTIVES

Obsessive-compulsive disorder (OCD) is a debilitating mental disorder characterized by persistent and intrusive thoughts accompanied by repetitive mental or physical acts. While both intolerance of uncertainty and emotion-related impulsivity have been consistently evidenced as cognitive risk factors of OCD, no studies have considered their joint effects. The current study examined the interaction between intolerance of uncertainty and two forms of emotion-related impulsivity-including both a behavioural and cognitive form-in predicting OCD symptoms.

DESIGN

Cross-sectional data were collected online from community-based adult participants.

METHODS

Participants (N = 673) completed a battery of self-report measures of OCD symptom severity, intolerance of uncertainty, and emotion-related impulsivity.

RESULTS

The behavioural form of emotion-related impulsivity positively moderated the relationship between intolerance of uncertainty and OCD symptoms. Elevated levels of both factors predicted the most severe symptoms, particularly checking, washing, and obsessing. This interaction effect was not found for the cognitive form of emotion-related impulsivity, which still emerged as a unique predictor of OCD symptom severity, specifically obsessing symptoms.

CONCLUSIONS

Current findings furthered the understanding of the link between intolerance of uncertainty and OCD symptoms by highlighting the role of emotion-related impulsivity. When uncertainty triggers distress in individuals with high intolerance of uncertainty, the urge to behaviourally alleviate this distress could promote the use of maladaptive obsessions and compulsions, leading to greater OCD symptoms. Results also indicated the potentially differential effects from the behavioural versus cognitive forms of emotion-related impulsivity on different symptom domains, and the mechanistic link here is worthy of further investigation.

Heuristic strategy of intuitive statistical inferences in 7- to 10-year-old children

Intuitive statistical inferences refer to making inferences about uncertain events based on limited probabilistic information, which is crucial for both human and non-human species' survival and reproduction. Previous research found that 7- and 8-year-old children failed in intuitive statistical inference tasks after heuristic strategies had been controlled. However, few studies systematically explored children's heuristic strategies of intuitive statistical inferences and their potential numerical underpinnings. In the current research, Experiment 1 (N = 81) examined 7- to 10-year-olds' use of different types of heuristic strategies; results revealed that children relied more on focusing on the absolute number strategy. Experiment 2 (N = 99) and Experiment 3 (N = 94) added continuous-format stimuli to examine whether 7- and 8-year-olds could make genuine intuitive statistical inferences instead of heuristics. Results revealed that both 7- and 8-year-olds and 9- and 10-year-olds performed better in intuitive statistical inference tasks with continuous-format stimuli, even after focusing on the absolute number strategy had been controlled. The results across the three experiments preliminarily hinted that the ratio processing system might rely on the approximate number system. Future research could clarify what specific numerical processing mechanism may be used and how it might support children's statistical intuitions.

On the uncertainty of the correlation between nanoparticle avidity and biodistribution

The specific delivery of a drug to its site of action also known as targeted drug delivery is a topic in the field of pharmaceutics studied for decades. One approach extensively investigated in this context is the use ligand functionalized nanoparticles. These particles are modified to carry receptor specific ligands, enabling them to accumulate at a desired target site. However, while this concept initially appears straightforward to implement, in-depth research has revealed several challenges hindering target site specific particle accumulation - some of which remain unresolved to this day. One of these challenges consists in the still incomplete understanding of how nanoparticles interact with biological systems. This knowledge gap significantly compromises the predictability of particle distribution in biological systems, which is critical for therapeutic efficacy. One of the most crucial steps in delivery is the attachment of nanoparticles to cells at the target site. This attachment occurs via the formation of multiple ligand receptor bonds. A process also referred to as multivalent interaction. While multivalency has been described extensively for individual molecules and macromolecules respectively, little is known on the multivalent binding of nanoparticles to cells. Here, we will specifically introduce the concept of avidity as a measure for favorable particle membrane interactions. Also, an overview about nanoparticle and membrane properties affecting avidity will be given. Thereafter, we provide a thorough review on literature investigating the correlation between nanoparticle avidity and success in targeted particle delivery. In particular, we want to analyze the currently uncertain data on the existence and nature of the correlation between particle avidity and biodistribution.

Facing uncertainties: The longitudinal relationship between childhood maltreatment and exploratory behavior

BACKGROUND

Exploratory behavior, as an essential component of decision-making, is indispensable for maximizing long-term benefits, making it a crucial factor in adolescents' psychological well-being and social adaptation. Despite the established understanding that this adaptive behavior is shaped by early adverse experiences, limited knowledge exists regarding the longitudinal relationship between childhood maltreatment and exploratory behavior.

OBJECTIVE

The present study examines whether childhood maltreatment would impede subsequent exploratory behavior, considering the mediating role of uncertainty stress and the moderating role of intolerance of uncertainty.

PARTICIPANTS AND SETTING

Participants were 655 adolescents from a longitudinal design with two waves spanning six months (Mage = 15.99, SDage = 0.92, 43.5 % female).

METHODS

Correlation analysis and longitudinal moderated mediation effect testing were used to test our hypotheses.

RESULTS

Correlation analysis indicated that childhood maltreatment was negatively correlated with exploratory behavior only simultaneously but not longitudinally. After controlling age and gender, childhood maltreatment would accompany higher levels of uncertainty stress, which in turn may act as a driving force behind subsequent exploratory behavior. The heightened intolerance of uncertainty may potentially mitigate the direct link between childhood maltreatment and later exploratory behavior. Furthermore, this trait amplifies the experienced uncertainty stress in individuals who have undergone maltreatment, thereby increasing their inclination toward engaging in subsequent exploratory behavior.

CONCLUSIONS

Given the critical role of uncertainty stress, promoting more exploration among these maltreated adolescents requires corresponding cognitive and behavioral interventions to adjust their perception and cognition of uncertainty.

Cryonics: Traps and transformations

Cryonics is the practice of cryopreserving the bodies or brains of legally dead individuals with the hope that these individuals will be reanimated in the future. A standard argument for cryonics says that cryonics is prudentially justified despite uncertainty about its success because at worst it will leave you no worse off than you otherwise would have been had you not chosen cryonics, and at best it will leave you much better off than you otherwise would have been. Thus, it is a good, no-risk bet; in game-theoretic terms, cryonics is a weakly dominant strategy relative to refraining from utilizing cryonics. I object to this argument for two reasons. First, I argue that there is a practically relevant chance that cryonics will put you into an inescapable and very bad situation. Hence, cryonics is neither a no-risk bet nor a weakly dominant strategy. Second, I argue that the experience of being reanimated and living in the distant future would likely be transformative, and this likelihood undermines your justification for thinking that reanimation would be beneficial to you. I conclude that the standard argument does not show that cryonics is prudentially justified.

Strategic social decision making undergoes significant changes in typically developing and autistic early adolescents

Information sampling about others' trustworthiness prior to cooperation allows humans to minimize the risk of exploitation. Here, we examined whether early adolescence or preadolescence, a stage defined as in between childhood and adolescence, is a significant developmental period for strategic social decisions. We also sought to characterize differences between autistic children and their typically developing (TD) peers. TD (N = 48) and autistic (N = 56) 8- to 12-year-olds played an online information sampling trust game. While both groups adapted their information sampling and cooperation to the various trustworthiness levels of the trustees, groups differed in how age and social skills modulated task behavior. In the TD group social skills were a stronger overall predictor of task behavior. In the autistic group, age was a stronger predictor and interacted with social skills. Computational modeling revealed that both groups used the same heuristic information sampling strategy-albeit older TD children were more efficient as reflected by decreasing decision noise with age. Autistic children had lower prior beliefs about the trustee's trustworthiness compared to TD children. These lower priors indicate that children believed the trustees to be less trustworthy. Lower priors scaled with lower social skills across groups. Notably, groups did not differ in prior uncertainty, meaning that the priors of TD and autistic children were equally strong. Taken together, we found significant development in information sampling and cooperation in early adolescence and nuanced differences between TD and autistic children. Our study highlights the importance of deep phenotyping of children including clinical measures, behavioral experiments and computational modeling. RESEARCH HIGHLIGHTS: We specified how early adolescents with and without an autism diagnosis sampled information about their interaction partners and made cooperation decisions in a strategic game. Early adolescence is a significant developmental period for strategic decision making, marked by significant changes in information sampling efficiency and adaptivity to the partner's behavior. Autistic and non-autistic groups differed in how age and social skills modulated task behavior; in non-autistic children behavior was more indicative of overall social skills. Computational modeling revealed differences between autistic and non-autistic groups in their initial beliefs about cooperation partners; autistic children expected their partners to be less trustworthy.

Negative life events and sleep disturbance among adolescents: Intolerance of uncertainty as mediator and moderator

BACKGROUND

While negative life events (NLEs) have been linked to an increased risk of sleep disturbance among adolescents, the mechanisms of this impact still lack further examination. The current study aimed to explore whether intolerance of uncertainty (IU), a dispositional transdiagnostic vulnerability factor for psychopathology, could act as a mediator and/or moderator in the link from NLEs to sleep disturbance.

METHODS

A longitudinal nested subsample of 54,240 Chinese adolescents (aged 9-19) were surveyed at baseline (Timepoint 1) and six months later (Timepoint 2). They completed questionnaires to assess their IU, NLEs, sleep disturbance and sociodemographic characteristics. Mediation and moderation analyses were conducted to test our hypotheses.

RESULTS

Upon adjusting for covariates, IU was found to mediate the relationship between NLEs and residual changes in sleep disturbance over a six-month period, with the mediation effect accounting for 31.8%. Additionally, the moderating role of IU in this relationship was also identified, suggesting that a high level of IU exacerbated the effect of NLEs on sleep disturbance.

CONCLUSIONS

In conclusion, our findings shed light on the dual roles of IU in the link from NLEs to sleep disturbance, holding significant practical implications for preventing and intervening in sleep disturbance among adolescents. To mitigate the risk of sleep disturbance among adolescents experiencing NLEs, timely assessments of IU and tailored interventions to enhance uncertainty tolerance are necessary.

Incorporating uncertainty in Indigenous sea Country monitoring with Bayesian statistics: Towards more informed decision-making

Partnerships in marine monitoring combining Traditional Ecological Knowledge and western science are developing globally to improve our understanding of temporal changes in ecological communities that better inform coastal management practices. A fuller communication between scientists and Indigenous partners about the limitations of monitoring results to identify change is essential to the impact of monitoring datasets on decision-making. Here we present a 5-year co-developed case study from a fish monitoring partnership in northwest Australia showing how uncertainty estimated by Bayesian models can be incorporated into monitoring management indicators. Our simulation approach revealed there was high uncertainty in detecting immediate change over the following monitoring year when translated to health performance indicators. Incorporating credibility estimates into health assessments added substantial information to monitoring trends, provided a deeper understanding of monitoring limitations and highlighted the importance of carefully selecting the way we evaluate management performance indicators.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Epistemic uncertainty in Bayesian predictive probabilities

Bayesian predictive probabilities have become a ubiquitous tool for design and monitoring of clinical trials. The typical procedure is to average predictive probabilities over the prior or posterior distributions. In this paper, we highlight the limitations of relying solely on averaging, and propose the reporting of intervals or quantiles for the predictive probabilities. These intervals formalize the intuition that uncertainty decreases with more information. We present four different applications (Phase 1 dose escalation, early stopping for futility, sample size re-estimation, and assurance/probability of success) to demonstrate the practicality and generality of the proposed approach.

Spatial multi-attention conditional neural processes

Spatial prediction tasks are challenging when observed samples are sparse and prediction samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction tasks and have the advantage of measuring the uncertainty of the interpolation result. However, as the sample size increases, GPs suffer from significant overhead. Standard neural networks (NNs) provide a powerful and scalable solution for modeling spatial data, but they often overfit small sample data. Based on conditional neural processes (CNPs), which combine the advantages of GPs and NNs, we propose a new framework called Spatial Multi-Attention Conditional Neural Processes (SMACNPs) for spatial small sample prediction tasks. SMACNPs are a modular model that can predict targets by employing different attention mechanisms to extract relevant information from different forms of sample data. The task representation is inferred by measuring the spatial correlation contained in different sample points and the relationship contained in attribute variables, respectively. The distribution of the target variable is predicted by GPs parameterized by NNs. SMACNPs allow us to obtain accurate predictions of the target value while quantifying the prediction uncertainty. Experiments on spatial prediction tasks on simulated and real-world datasets demonstrate that this framework flexibly incorporates spatial context and correlation into the model, achieving state-of-the-art results in spatial small sample prediction tasks in terms of both predictive performance and reliability. For example, on the California housing dataset, our method reduces MAE by 8% and MSE by 7% compared to the second-best method. In addition, a spatiotemporal prediction task to forecast traffic speed further confirms the effectiveness and generality of our method.

The role of vehicle movement in swine disease dissemination: Novel method accounting for pathogen stability and vehicle cleaning effectiveness uncertainties

Several propagation routes drive animal disease dissemination, and among these routes, contaminated vehicles traveling between farms have been associated with indirect disease transmission. In this study, we used near-real-time vehicle movement data and vehicle cleaning efficacy to reconstruct the between-farm dissemination of the African swine fever virus (ASFV). We collected one year of Global Positioning System data of 823 vehicles transporting feed, pigs, and people to 6363 swine production farms in two regions in the U.S. Without cleaning, vehicles connected up to 2157 farms in region one and 437 farms in region two. Individually, in region one vehicles transporting feed connected 2151 farms, pigs to farms 2089 farms, pigs to market 1507 farms, undefined vehicles 1760 farm, and personnel three farms. The simulation results indicated that the contact networks were reduced the most for crew transport vehicles with a 66% reduction, followed by vehicles carrying pigs to market and farms, with reductions of 43% and 26%, respectively, when 100% cleaning efficacy was achieved. The results of this study showed that even when vehicle cleaning and disinfection are 100% effective, vehicles are still connected to numerous farms. This emphasizes the importance of better understanding transmission risks posed by vehicles to the swine industry and regulatory agencies.

A qualitative study of the experiences and perceptions of older patients and relatives prior to cardiac surgery

BACKGROUND

Shared decision-making plays an important role in ensuring value-based healthcare in cardiac surgery. However, the personal situations of patients in cardiac care have not been widely explored, and thus, little is known about the decision-making experiences of patients and their relatives before surgery.

OBJECTIVE

To explore the perceptions of patients indicated for cardiac surgery and their relatives during the decision-making process, as well as their experiences of a conversation aimed at achieving shared decision-making in the treatment trajectory.

METHODS

The data were collected through semi-structured in-depth interviews with patients aged ≥70 years who were indicated for cardiac surgery and their relatives until theme saturation. Both inductive and deductive analysis were conducted based on the principles of reflexive thematic analysis.

RESULTS

Interviews with 16 patients and 10 relatives provided in-depth insights into the experiences of patients and their relatives in terms of a shared decision-making process prior to surgery. Overall, 15 subthemes were identified, and these were divided into three themes. In general, the patients' experiences and perceptions were influenced by their (1) general daily functioning. The relatives were more concerned about (2) social expectations and (3) existential uncertainty.

CONCLUSIONS

Patients eligible for cardiac surgery and their families have unique experiences and perceptions during the process of shared decision-making. The subthemes emerging from this study, such as the overestimation of potential medical outcomes by patients and their relatives, who experience fear about the current health situation of their loved one, require careful attention from healthcare professionals during decision-making conversations.

Uncertain and Anxiously Searching for Answers: The Roles of Negative HealthCare Experiences and Medical Mistrust in Intentions to Seek Information from Online Spaces

While online sources of information, like support groups and wellness influencers, can be beneficial for those seeking additional information about their health conditions, these sources can also contain detrimental information. As misinformation and even conspiracies like QAnon proliferate in wellness discourse, particularly in online support groups and on the accounts of wellness influencers, it becomes increasingly important to understand what may contribute to individuals seeking information from these sources. Based on uncertainty in illness theory and the theory of motivated information management, we conducted a cross-sectional survey (N = 544) to test the role of negative health-care experiences and medical mistrust in uncertainty and information seeking from online support groups and wellness influencers across those with chronic and acute health concerns. Results indicated that negative health-care experiences had an indirect effect on information seeking from both online support groups and wellness influencers. This indirect effect, however, operated through uncertainty anxiety but not uncertainty discrepancy. For those with chronic conditions, the indirect effect also included medical mistrust. Implications and future extensions of the results are discussed.

Impact of uncertainties in biometric parameters on intraocular lens power formula predicted refraction using a Monte-Carlo simulation

PURPOSE

The purpose of this study was to investigate the uncertainty in the formula predicted refractive outcome REFU after cataract surgery resulting from measurement uncertainties in modern optical biometers using literature data for within-subject standard deviation Sw.

METHODS

This Monte-Carlo simulation study used a large dataset containing 16 667 preoperative IOLMaster 700 biometric measurements. Based on literature Sw values, REFU was derived for both the Haigis and Castrop formulae using error propagation strategies. Using the Hoya Vivinex lens (IOL) as an example, REFU was calculated both with (WLT) and without (WoLT) consideration of IOL power labelling tolerances.

RESULTS

WoLT the median REFU was 0.10/0.12 dpt for the Haigis/Castrop formula, and WLT it was 0.13/0.15 dpt. WoLT REFU increased systematically for short eyes (or high power IOLs), and WLT this effect was even more pronounced because of increased labelling tolerances. WoLT the uncertainty in the measurement of the corneal front surface radius showed the largest contribution to REFU, especially in long eyes (and low power IOLs). WLT the IOL power uncertainty dominated in short eyes (or high power IOLs) and the uncertainty of the corneal front surface in long eyes (or low power IOLs).

CONCLUSIONS

Compared with published data on the formula prediction error of refractive outcome after cataract surgery, the uncertainty of biometric measures seems to contribute with ⅓ to ½ to the entire standard deviation. REFU systematically increases with IOL power and decreases with axial length.

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.

Trait and situation-specific intolerance of uncertainty predict affective symptoms during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic, a high-uncertainty situation, presents an ideal opportunity to examine how trait intolerance of uncertainty (IU) and situation-specific IU relate to each other and to mental health outcomes. The current longitudinal study examined the unique associations of trait and COVID-specific IU with general distress (anxiety and depression) and pandemic-specific concerns (pandemic stress and vaccine worry).

METHODS

A community sample of Florida adults (N = 2152) was surveyed online at three timepoints. They completed measures of trait IU at Wave 1 (April-May 2020) and COVID-specific IU at Wave 2 (May-June 2020). At Wave 3 (December-February 2021), they reported symptoms of depression, anxiety, pandemic stress, and vaccine worry.

RESULTS

We used structural equation modeling to test our overall model. Trait IU significantly predicted later COVID-specific IU, however there was no significant effect of trait IU on any outcome measure after accounting for COVID-specific IU. Notably, COVID-specific IU fully mediated the relationship between trait IU and all four symptom measures.

LIMITATIONS

There were several limitations of the current study, including the use of a community sample and high participant attrition.

CONCLUSIONS

Results suggest that COVID-specific IU predicts mental health outcomes over and above trait IU, extending the existing literature. These findings indicate that uncertainty may be more aversive when it is related to specific distressing situations, providing guidance for developing more specific and individualized interventions. Idiographic treatments which target situation-specific IU may be more efficacious in reducing affective symptoms and related stress during the COVID-19 pandemic or other similar events.

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information

Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable policies for agricultural water resources allocation under multiple uncertainties. Random-fuzzy and interval information in water resources system that have directly impact on the effectiveness of management schemes is reflected through interval two-stage stochastic fuzzy-probability programming. The model was root from regional agricultural water resources system in Jining City, China under considering the relationship among effective precipitation, crop water demand, and pollutants emission. Two types policies (water consumption-control and pollutants emission-control) are designed for searching the related interaction on water resources management and water quality improvement. The results indicated that water resources policies would be of water and environmental double benefits, and a large rainfall would reduce irrigation amount from water sources and lead to a larger pollutants emission trading. The results will help for defining scientific and effective water resources protection and management policies and analyzing the related interacted effects on water consumption, pollutants control and system benefit.

Bayesian interpretation of the prefrontal P2 ERP component based on stimulus/response mapping uncertainty

The brain can be seen as a predictive system continuously computing prior information to guess posterior probabilities minimizing sources of uncertainty. To test this Bayesian view of the brain, event-related potentials (ERP) methods have been used focusing on the well-known P3 component, traditionally associated with decision-making processes and sources of uncertainty regarding target probability. Another ERP component linked with decision-making is the prefrontal P2 (pP2) component, which has never been considered within the Bayesian framework. To test which source of uncertainty could be associated with the pP2, uncertainty induced by target probability and stimulus-response (S/R) mapping were modulated in three visuomotor tasks. Results showed that the pP2 had the largest amplitude in the task with the largest uncertainty regarding the S/R mapping and degraded as the S/R mapping became more predictable. The P3 was maximal in the tasks with larger uncertainty regarding the target probability. While we confirmed the P3 association with target probability, we extended our knowledge on the pP2 associating it with S/R mapping uncertainty. This component, which has been previously localized within the anterior insular cortex, may minimize S/R mapping uncertainty allowing response-related evidence accumulation and comparing current events with internal representations to extract action-related probabilities.

How to evaluate uncertainty estimates in machine learning for regression?

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output a prediction interval directly. These methods are often tested by examining the fraction of test points that fall inside the corresponding prediction intervals. Intuitively, both approaches seem logical. However, we demonstrate through both theoretical arguments and simulations that both ways of evaluating the quality of uncertainty estimates have serious flaws. Firstly, both approaches cannot disentangle the separate components that jointly create the predictive uncertainty, making it difficult to evaluate the quality of the estimates of these components. Specifically, the quality of a confidence interval cannot reliably be tested by estimating the performance of a prediction interval. Secondly, the loglikelihood does not allow a comparison between methods that output a prediction interval directly and methods that output a density. A better loglikelihood also does not necessarily guarantee better prediction intervals, which is what the methods are often used for in practice. Moreover, the current approach to test prediction intervals directly has additional flaws. We show why testing a prediction or confidence interval on a single test set is fundamentally flawed. At best, marginal coverage is measured, implicitly averaging out overconfident and underconfident predictions. A much more desirable property is pointwise coverage, requiring the correct coverage for each prediction. We demonstrate through practical examples that these effects can result in favouring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence or prediction intervals. Finally, we propose a simulation-based testing approach that addresses these problems while still allowing easy comparison between different methods. This approach can be used for the development of new uncertainty quantification methods.

The practice of historical ecology: What, when, where, how and what for

In recent decades, there has been a growing number of studies exploring the historical dimensions of the interconnectedness of human societies and the environment. A core approach in this field is historical ecology. We analyzed 544 historical-ecological papers to assess patterns and trends in the field. We found a high degree of interdisciplinarity with a focus on local case studies, of periods of fewer than 500 years, analyzing archival sources through quantitative approaches. The proportion of papers containing management recommendations has increased over time. To make historical ecology globally relevant, more effort should be made to utilize studies across languages, borders and worldviews. We call for high standards regarding the use of social scientific methodologies. Lastly, we argue that fostering longer-term studies and assessing the real-life impact of policy recommendations emerging from historical ecology can help the discipline better contribute solutions to the challenges facing humanity in an uncertain future.

The structure and development of explore-exploit decision making

A critical component of human learning reflects the balance people must achieve between focusing on the utility of what they know versus openness to what they have yet to experience. How individuals decide whether to explore new options versus exploit known options has garnered growing interest in recent years. Yet, the component processes underlying decisions to explore and whether these processes change across development remain poorly understood. By contrasting a variety of tasks that measure exploration in slightly different ways, we found that decisions about whether to explore reflect (a) random exploration that is not explicitly goal-directed and (b) directed exploration to purposefully reduce uncertainty. While these components similarly characterized the decision-making of both youth and adults, younger participants made decisions that were less strategic, but more exploratory and flexible, than those of adults. These findings are discussed in terms of how people adapt to and learn from changing environments over time.Data has been made available in the Open Science Foundation platform (osf.io).

Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial

Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy-to-use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short-term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time-series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included.

Measurement uncertainty estimation of free drug concentrations in clinical laboratories using equilibrium dialysis

OBJECTIVES

Developing procedures based on equilibrium dialysis (ED) that allow measuring the free drug concentration in plasma improves therapeutic drug monitoring (TDM) in those cases where its measurement is justified. However, this procedure requires specific sample preparation and presents different pitfalls, which are not error-free. As with any result provided by a clinical laboratory, this one should be as accurate as possible to allow a correct clinical interpretation. The measurement uncertainty (MU) is a parameter that enables the accuracy of results to be known, and that is mandated by ISO 15189. Herein, this study suggests how the MU for the results of the free drug concentrations in serum could be estimated when an ED is used.

METHODS

A combination of the top-down and bottom-up approaches was used to estimate the MU based on the ISO/TS 20914:2019 and JCGM 100:2008 guidelines, including the concentration of free phenytoin in serum, as an example. Different scenarios were incorporated considering or not a significant bias related to the primary drawbacks of ED: the non-specific binding, the volume shift effect and the Gibbs-Donnan effect.

RESULTS

The expanded uncertainties estimated ranged between 13.0 and 30.9 %. The highest MU corresponded to the free drug concentrations in serum results when significant biases related to the volume shift and Gibbs-Donnan effects exist.

CONCLUSIONS

A detailed estimation of MU for free drug concentrations is presented using ED, considering different scenarios. This study could stimulate clinical laboratories to perform MU studies and its application in TDM.

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

Implementation of a new EGSnrc particle source class for computed tomography: validation and uncertainty quantification

Objective. Personalized dose monitoring and risk management are of increasing significance with the growing number of computer tomography (CT) examinations. These require high-quality Monte Carlo (MC) simulations that are of the utmost importance for the new developments in personalized CT dosimetry. This work aims to extend the MC framework EGSnrc source code with a new particle source. This, in turn, allows CT-scanner-specific dose and image calculations for any CT scanner. The novel method can be used with all modern EGSnrc user codes, particularly for the simulation of the effective dose based on DICOM images and the calculation of CT images.Approach. The new particle source can be used with input data derived by the user. The input data can be generated by the user based on a previously developed method for the experimental characterization of any CT scanner (doi.org/10.1016/j.ejmp.2015.09.006). Furthermore, the new particle source was benchmarked by air kerma measurements in an ionization chamber at a clinical CT scanner. For this, the simulated angular distribution and attenuation characteristics were compared to measurements to verify the source output free in air. In a second validation step, simulations of air kerma in a homogenous cylindrical and an anthropomorphic thorax phantom were performed and validated against experimentally determined results. A detailed uncertainty evaluation of the simulated air kerma values was developed.Main results. We successfully implemented a new particle source class for the simulation of realistic CT scans. This method can be adapted to any CT scanner. For the attenuation characteristics, there was a maximal deviation of 6.86% between the measurement and the simulation. The mean deviation for all tube voltages was 2.36% (σ= 1.6%). For the phantom measurements and simulations, all the values agreed within 5.0%. The uncertainty evaluation resulted in an uncertainty of 5.5% (k=1).

[Estimation of Uncertainty of the VMAT Absolute Dose Measurement Due to the Phantom Setup Error Using a Treatment Planning System]

PURPOSE

When performing single-point dose verification in VMAT, it is necessary to avoid the regions with steep dose gradient. We propose a method to obtain the estimated value ( Uplan) of uncertainty of the absolute dose measurement due to the phantom setup error by using dose gradient calculated from treatment planning system (TPS), for evaluating the appropriate measurement points.

METHODS

The dose gradient was calculated from the planned dose values in the vicinity of the isocenter point using TPS. The phantom setup error was estimated. The Uplan was calculated using the proposed formula after estimating the phantom setup error. Then, the dose gradient was calculated from the measured dose values in the vicinity of the isocenter point specified by TPS using the Tough water phantom with ionization chamber (IC), and Umeas was calculated as in Uplan.

RESULTS

The correlation coefficient between Uplan and Umeas was 0.984, which indicates a high correlation. The average of the difference between Umeas and Uplan was -0.24%. We considered that this result was caused by the influence of volume averaging effect of IC.

CONCLUSION

The Uplan obtained from this proposed method reflects the uncertainty of the absolute dose measurement due to the phantom setup error and is useful for evaluating the appropriate measurement points for absolute dose measurement.

Influence of age and cognitive demand on motor decision making under uncertainty: a study on goal directed reaching movements

In everyday life, we constantly make decisions about actions to be performed subsequently. Research on motor decision making has provided empirical evidence for an influence of decision uncertainty on movement execution in young adults. Further, decision uncertainty was suggested to be increased in older adults due to limited cognitive resources for the integration of information and the prediction of the decision outcomes. However, the influence of cognitive aging on decision uncertainty during motor decision making and movement execution has not been investigated, yet. Thus, in the current study, we presented young and older adults with a motor decision making task, in which participants had to decide on pointing towards one out of five potential targets under varying cognitive demands. Statistical analyses revealed stronger decreases in correctly deciding upon the pointing target, i.e. task performance, from low to higher cognitive demand in older as compared to young adults. Decision confidence also decreased more strongly in older adults with increasing cognitive demand, however, only when collapsing across correct and incorrect decision trials, but not when considering correct decision trials, only. Further, older adults executed reaching movements with longer reaction times and increased path length, though the latter, again, not when considering correct decision trials, only. Last, reaction time and variability in movement execution were both affected by cognitive demand. The outcomes of this study provide a differentiated picture of the distinct and joint effects of aging and cognitive demand during motor decision making.

Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters

BACKGROUND

Breeding queens may be mated with drones that are produced by a single drone-producing queen (DPQ), or a group of sister-DPQs, but often only the dam of the DPQ(s) is reported in the pedigree. Furthermore, datasets may include colony phenotypes from DPQs that were open-mated at different locations, and thus to a heterogeneous drone population.

METHODS

Simulation was used to investigate the impact of the mating strategy and its modelling on the estimates of genetic parameters and genetic trends when the DPQs are treated in different ways in the statistical evaluation model. We quantified the bias and standard error of the estimates when breeding queens were mated to one DPQ or a group of DPQs, assuming that this information was known or not. We also investigated four alternative strategies to accommodate the phenotypes of open-mated DPQs in the genetic evaluation: excluding their phenotypes, adding a dummy pseudo-sire in the pedigree, or adding a non-genetic (fixed or random) effect to the statistical evaluation model to account for the origin of the mates.

RESULTS

The most precise estimates of genetic parameters and genetic trends were obtained when breeding queens were mated with drones of single DPQs that are correctly assigned in the pedigree. However, when they were mated with drones from one or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in these estimates. Furthermore, genetic variances were considerably overestimated when phenotypes of colonies from open-mated DPQs were adjusted for their mates by adding a dummy pseudo-sire in the pedigree for each subpopulation of open-mating drones. On the contrary, correcting for the heterogeneous drone population by adding a non-genetic effect in the evaluation model produced unbiased estimates.

CONCLUSIONS

Knowing only the dam of the DPQ(s) used in each mating may lead to erroneous assumptions on how DPQs were used and severely bias the estimates of genetic parameters and trends. Thus, we recommend keeping track of DPQs in the pedigree, and not only of the dams of DPQ(s). Records from DPQ colonies with queens open-mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

A comparative analysis of Pakistan and Kurdistan Region of Iraq

The objective of the study is to explore the relationship between country governance practices along with political stability and Economic policy uncertainty, and stock market performance of two different economies, Pakistan and Kurdistan region of Iraq. To meet our objectives, we used the 25 years past data from 1996 to 2021. Data is collected from the DataStream database. The regression analysis is used as the method of estimation for linear and moderation effect. Our results show that regulatory quality, rules of law and political stability has significant positive relationship with stock market performance of Pakistan, but all the governance indicators have significant positive relationship with stock market performance of the Kurdistan Region of Iraq. Moreover, political stability has significant moderating impact between the governance practices and the performance of the stock markets of both economies indicating that the governance practices perform well with the political stability that leads to rise in the stock market indices of selected countries. Economic policy uncertainty has significant negative moderation impact due to creating the risk in both economies that decrease the performance of the stock markets of the selected economies. Finally, our study advocated some implications for the investors to increase their confidence on the stock of high political stability and low economic policy uncertainty economies. Government can take significant measures to control the uncertainty of the policy and portfolio managers can adjust their risk on the ground of the political stability and efficient governance practices countries.

Dispositional factors in the explanation of symptoms of depression, anxiety, health anxiety and COVID-19 Phobia

Maladaptive personality, the motivational systems, and intolerance of uncertainty play important roles in the statistical explanation of depression and anxiety. Here, we notably examined for the first time whether symptoms of depression, anxiety, health anxiety, and fear of COVID-19 share similar associations (e.g., variance explained) with these important dispositional dimensions. For this cross-sectional study, data from 1001 participants recruited in Germany (50% women; mean age = 47.26) were collected. In separate models, we examined the cross-sectional associations of the symptoms of depression, anxiety, health anxiety, and fear of COVID-19 with the Personality Inventory for DSM Short Form Plus scales, the Behavioral Inhibition System / Flight-Fight-Freeze System / Behavioral Activation System scales, and Intolerance of Uncertainty scales. Relative weight analyses were used to determine the within-model importance of the different scales in the prediction of the symptoms. All in all, our study showed that maladaptive personality and intolerance of uncertainty dimensions are more important sets of predictors of the studied outcomes (with which depressive and anxious symptomatology feature very similar associations) than are the motivational system dimensions. Within predictor sets, the scales with the most important predictors were: Negative Affectivity, the Behavioral Inhibition System, and Burden due to Intolerance of Uncertainty. Our findings highlight the relevance of focusing behavioral targets of psychotherapy on these within-set traits and identify potential research priorities (maladaptive personality and intolerance of uncertainty) in relation to the symptoms of interest.

Priorities for improving predictions of vessel-mediated marine invasions

Nonindigenous marine species are impacting the integrity of marine ecosystems worldwide. The invasion rate is increasing, and vessel traffic, the most significant human-assisted transport pathway for marine organisms, is predicted to double by 2050. The ability to predict the transfer of marine species by international and domestic maritime traffic is needed to develop cost-effective proactive and reactive interventions that minimise introduction, establishment and spread of invasive species. However, despite several decades of research into vessel-mediated species transfers, some important knowledge gaps remain, leading to significant uncertainty in model predictions, often limiting their use in decision making and management planning. In this review, we discuss the sequential ecological process underlying human-assisted biological invasions and adapt it in a marine context. This process includes five successive stages: entrainment, transport, introduction, establishment, and the subsequent spread. We describe the factors that influence an organism's progression through these stages in the context of maritime vessel movements and identify key knowledge gaps that limit our ability to quantify the rate at which organisms successfully pass through these stages. We then highlight research priorities that will address these knowledge gaps and improve our capability to manage biosecurity risks at local, national and international scales. We identified four major data and knowledge gaps: (1) quantitative rates of entrainment of organisms by vessels; (2) the movement patterns of vessel types lacking maritime location devices; (3) quantifying the release (introduction) of organisms as a function of vessel behaviour (e.g. time spent at port); and (4) the influence of a species' life history on establishment success, for a given magnitude of propagule pressure. We discuss these four research priorities and how they can be addressed in collaboration with industry partners and stakeholders to improve our ability to predict and manage vessel-mediated biosecurity risks over the coming decades.

Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling

Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.

Identification and quantification of medical device extractables and leachables via non-target analysis (NTA); Analytical uncertainty

Leachables are substances that are leached from a medical device during its clinical use and are important due to the patient health-related effects they may have. Thus, medical devices are profiled for leachables (and/or extractables as probable leachables) to assess their potential impact on patient health and safety. This profiling is accomplished by screening extracts or leachates of the medical device for released organic substances via non-targeted analysis (NTA) employing chromatographic methods coupled with mass spectrometric detection. Chromatographic mass spectral response factors (RFs) for extractables and leachables vary significantly from compound to compound, complicating the quantitation of these compounds and the application of assessment strategies such as the Analytical Evaluation Threshold (AET). The analytical uncertainty resulting from response factor variation can be expressed in terms of an uncertainty factor (UF), which estimates the magnitude of response factor variation. This manuscript discusses the concept and impact of analytical uncertainty and provides best practice recommendations for the calculation and use of the uncertainty factor, UF.

The mediating role of hope in the relation between uncertainty and social support with self-management among patients with ESKD undergoing hemodialysis

BACKGROUND

Self-management behaviours are critical for patients requiring regular hemodialysis (HD) therapy. This study aimed to test the relationship between social support, uncertainty and self-management among HD patients and to explore whether hope plays a mediating role.

METHODS

In a cross-sectional study, a convenience sample of 212 HD patients from two hospitals completed the Perceived Social Support Scale (PSSS), Herth Hope Index (HHI), Short form Mishel Uncertainty in Illness Scale (SF-MUIS), and hemodialysis Self-Management Instrument (HD-SMI). Data were analysed using structural equation modelling.

RESULTS

The main finding indicated that social support positively affected self-management (β = 0.50, t = 4.97, p < 0.001), and uncertainty negatively affected self-management (β =-0.37, t=-4.12, p = < 0.001). In mediational model analysis, the effect of social support on self-management was fully mediated [(β = 0.12; 95% BC CI (0.047, 0.228)] by hope. Also, the effect of uncertainty on self-management was fully mediated [(β=- 0.014; 95% BC CI (-0.114, -0.003)] by hope.

CONCLUSIONS

"Considering factors influencing self-management in HD patients is crucial for improving quality of life. Receiving support and informational resources can not only foster hope but also reduce their uncertainty, thus aiding in enhancing clinical outcomes, quality of life, and reducing complications. "Health care providers, especially nurses were advised to accept the existence of uncertainty, help patients make optimal use of support resources, and give more importance to disambiguation to reassure them. Therefore, well-designed interventions that enhance social support and hope and reduce uncertainty may help improve self-management behaviour in HD patients.

Insomnia is more likely to persist than remit after a time of stress and uncertainty: a longitudinal cohort study examining trajectories and predictors of insomnia symptoms

STUDY OBJECTIVES

The study aimed to characterize insomnia symptom trajectories over 12 months during a time of stress and uncertainty, the coronavirus disease 2019 (COVID-19) pandemic. It also aimed to investigate sleep and psychological predictors of persistent insomnia symptoms.

METHODS

This longitudinal cohort study comprised 2069 participants with and without insomnia symptoms during the first year of the pandemic. Participants completed online surveys investigating sleep, insomnia, and mental health at four timepoints over 12 months (April 2020-May 2021). Additional trait-level cognitive/psychological questionnaires were administered at 3 months only.

RESULTS

Six distinct classes of insomnia symptoms emerged: (1) severe persistent insomnia symptoms (21.65%), (2) moderate persistent insomnia symptoms (32.62%), (3) persistent good sleep (32.82%), (4) severe insomnia symptoms at baseline but remitting over time (2.27%), (5) moderate insomnia symptoms at baseline but remitting over time (7.78%), and (6) good sleep at baseline but deteriorating into insomnia symptoms over time (2.85%). Persistent insomnia trajectories were predicted by high levels of sleep reactivity, sleep effort, pre-sleep cognitive arousal, and depressive symptoms at baseline. A combination of high sleep reactivity and sleep effort reduced the odds of insomnia remitting. Higher sleep reactivity also predicted the deterioration of good sleep into insomnia symptoms over 12 months. Lastly, intolerance of uncertainty emerged as the only trait-level cognitive/psychological predictor of insomnia trajectory classes.

CONCLUSIONS

Insomnia was more likely to persist than remit over the first year of the COVID-19 pandemic. Addressing sleep reactivity and sleep effort appears critical for reducing insomnia persistence rates after times of stress and uncertainty.

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy

Objective.In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Approach.Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed.Main results.For the full test set, with respect to the MC reference, the predicted prostateD90metric had mean differences of -0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [-1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cchad the largest uncertainty metric of 0.0042.Significance.The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.

[Causes of death: the uncertainty behind the numbers]

When a person dies in the Netherlands it is legally required to report the cause of death. In most cases however, there is uncertainty when classifying causes of death. Additional postmortem diagnostics such as a CT scan or autopsy do not always provide absolute certainty. Data on causes of death can be used to determine what are, on a population level, relevant health problems. One must be cautious to fully rely on these data for making policy or financing healthcare and research. Firstly, incorrectly classifying the cause of death can give a distorted view of the underlying causes. Secondly, relevant health problems, such as obesity, might be overlooked in the statistics when they are not clearly a cause of death.

Development and psychometric evaluation of uncertainty about disease and treatment scale in hemodialysis patients: a sequential-exploratory mixed-method study

BACKGROUND AND OBJECTIVE

The need for long-term treatment and frequent visits to treatment centers for hemodialysis can lead to psychological problems such as Uncertainty about Disease and Treatment (UC about D&T) in patients with chronic kidney failure. In order to understand uncertainty about disease and treatment and to plan for preventive measures and care interventions in various dimensions, there is a need for reliable and valid tools. The present study was conducted to design and psychometrically evaluate the Uncertainty about Disease and Treatment Scale (UC about D&TS) in patients undergoing hemodialysis.

METHODS

This study is of a methodological type and conducted in two stages. The first stage included a deductive (literature review) and an inductive approach (face-to-face interviews). In the second stage, psychometric indices of the UC about D&TS, including face validity (qualitative-quantitative), content validity (qualitative-quantitative), construct validity (exploratory factor analysis), and reliability (using Cronbach's alpha and McDonald's omega) were examined.

RESULTS

In the literature review stage, 66 items were extracted, and in the qualitative stage, 48 items were extracted. After merging similar items, 29 items were entered into the psychometric process. No items were removed in the face and content validity stages. In the construct validity stage, five factors were extracted, including self-uncertainty, uncertain situation, uncertain future, uncertainty of treatment outcomes, and information uncertainty, which constituted a total of 82.16% of the total variance. In this stage, five items were removed from the study due to a corrected item-total correlation below 0.32, and four items were removed due to cross-loading. The α and Ω were calculated as 0.828 and 0.818, respectively. The measurement stability and standard error of measurement were estimated at 0.977 and 2.019, respectively.

CONCLUSION

The results showed that the UC about D&TS is a valid and reliable measure for patients undergoing hemodialysis. This scale is specifically designed to measure UC about D&T in hemodialysis patients, and it is recommended that healthcare providers (Hcps) use this scale in follow-up visits.

The effects of prefrontal tDCS and hf-tRNS on the processing of positive and negative emotions evoked by video clips in first- and third-person

The causal role of the cerebral hemispheres in positive and negative emotion processing remains uncertain. The Right Hemisphere Hypothesis proposes right hemispheric superiority for all emotions, while the Valence Hypothesis suggests the left/right hemisphere's primary involvement in positive/negative emotions, respectively. To address this, emotional video clips were presented during dorsolateral prefrontal cortex (DLPFC) electrical stimulation, incorporating a comparison of tDCS and high frequency tRNS stimulation techniques and manipulating perspective-taking (first-person vs third-person Point of View, POV). Four stimulation conditions were applied while participants were asked to rate emotional video valence: anodal/cathodal tDCS to the left/right DLPFC, reverse configuration (anodal/cathodal on the right/left DLPFC), bilateral hf-tRNS, and sham (control condition). Results revealed significant interactions between stimulation setup, emotional valence, and POV, implicating the DLPFC in emotions and perspective-taking. The right hemisphere played a crucial role in both positive and negative valence, supporting the Right Hemisphere Hypothesis. However, the complex interactions between the brain hemispheres and valence also supported the Valence Hypothesis. Both stimulation techniques (tDCS and tRNS) significantly modulated results. These findings support both hypotheses regarding hemispheric involvement in emotions, underscore the utility of video stimuli, and emphasize the importance of perspective-taking in this field, which is often overlooked.

Nurses' perception of uncertainty regarding suspected pain in people with dementia: A qualitative descriptive study

OBJECTIVES

This study aims to qualitatively examine nurses' perception of uncertainty regarding suspected pain in people with dementia (PWD).

DESIGN

The study utilized a qualitative descriptive design.

PARTICIPANTS

The participants in this study were nurses with a minimum of six months of experience caring for PWD and currently working in a university hospital in Irbid, Jordan. Twenty-five participants were selected using convenience sampling from the selected hospital.

RESULTS

Four major themes and 12 subthemes relating to nurses' perceptions of uncertainty regarding suspected pain in PWD emerged. The main themes were (a) the culture-bound nature of uncertainty regarding suspected pain in PWD, (b) dimensions of uncertainty regarding suspected pain in PWD, (c) indicators of uncertainty regarding suspected pain in PWD, and (d) assessment methods of uncertainty to suspected pain in PWD. Moreover, for each major theme, different subthemes were developed.

CONCLUSIONS

It is crucial to address factors influencing -nurses' uncertainty regarding suspected pain in PWD to improve pain assessment and management in PWD. Additionally, the study identified five indicators of uncertainty: complicated decision-making, knowledge deficit, bias, intuition, and misconceptions. Effective assessment methods, such as semi-structured interviews and simulated assessments, should be employed to evaluate uncertainty accurately. By addressing these issues and utilizing appropriate assessment approaches, healthcare professionals can enhance pain management for individuals with dementia.

Uncertainty Work: Dealing with a Psychiatric Crisis in Two European Community Mental Health Teams

The quest for how to deal with a crisis in a community setting, with the aim of deinstitutionalizing mental health care, and reducing hospitalization and coercion, is important. In this article, we argue that to understand how this can be done, we need to shift the attention from acute moments to daily uncertainty work conducted in community mental health teams. By drawing on an empirical ethics approach, we contrast the modes of caring of two teams in Utrecht and Trieste. Our analysis shows how temporality structures, such as watchful waiting, are important in dealing with the uncertainty of a crisis.

Uncertainty among patients with advanced-stage lung cancer

BACKGROUND

Uncertainty is the inability to define the meaning of illness-related events, which may result in anxiety, depression, poor coping, the self-perception of being a burden and low quality of life. Uncertainty among Thai patients with advanced-stage lung cancer (ASLC) has not been well documented.

AIMS

To assess uncertainty in patients with ASLC.

METHODS

A cross-sectional survey design was adopted. Data were collected from 60 patients with ASLC at a university hospital. A demographic data form and the Mishel Uncertainty in Illness Scale (MUIS) were used to collect data. The data were analysed using descriptive statistics.

RESULTS

The patients had moderate levels of uncertainty in illness (83.73±15.25). Ambiguity about the illness and unpredictability of the prognosis scored at a moderate level for patients, while complexity of treatment and the system of care and inconsistency or lack of information, about the diagnosis or severity of the illness were at a low level.

CONCLUSION

The results of this study may help healthcare professionals better understand and manage uncertainty in patients with ASLC.