Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters

The effect of different dietary restriction on weight management and metabolic parameters in people with type 2 diabetes mellitus: a network meta-analysis of randomized controlled trials

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a globally prevalent chronic condition. Individuals with T2DM are at increased risk of developing complications associated with both macrovascular and microvascular pathologies. These comorbidities reduce patient quality of life and increase mortality. Dietary restriction is a principal therapeutic approach for managing T2DM. This study assessed the effects of various dietary regimens on body weight and metabolic profiles in T2DM patients, aiming to determine the most beneficial interventions for enhancing clinical outcomes and overall well-being.

METHODS

We conducted a literature search in PubMed, Embase, and Web of Science from 2003 to April 15, 2024. The risk of bias was assessed via the Revised Cochrane risk-of-bias tool for randomized trials (RoB2). The certainty of the evidence was appraised via the confidence in network meta-analysis (CINeMA) framework. Intermittent fasting (IF) was directly compared with continuous energy restriction (CER) via Review Manager 5.4. Network meta-analysis was statistically assessed via R Studio 4.3.3 and STATA 14.0.

RESULTS

Eighteen studies involving 1,658 participants were included. The network meta-analysis indicated that intermittent energy restriction, the twice-per-week fasting, time-restricted eating, fasting-mimicking diets (FMD), and CER interventions were more effective than conventional diets. Direct comparisons revealed that IF was as effective as CER for reducing glycated haemoglobin A1c, body weight, and body mass index. The results of the cumulative ranking analysis demonstrated that FMD had the greatest combined intervention effect, followed by TRE in terms of overall effectiveness.

CONCLUSIONS

Both IF and CER exert positive influences on weight control and metabolic profile enhancement in individuals with T2DM, with FMD as part of IF demonstrating the greatest impact. To substantiate these findings, more rigorous randomized controlled trials that directly compare the effects of the different IF regimens with one another and with the CER regimen are needed.

Automated grading system for quantifying KOH microscopic images in dermatophytosis

Concerning the progression of dermatophytosis and its prognosis, quantification studies play a significant role. Present work aims to develop an automated grading system for quantifying fungal loads in KOH microscopic images of skin scrapings collected from dermatophytosis patients. Fungal filaments in the images were segmented using a U-Net model to obtain the pixel counts. In the absence of any threshold value for pixel counts to grade these images as low, moderate, or high, experts were assigned the task of manual grading. Grades and corresponding pixel counts were subjected to statistical procedures involving cumulative receiver operating characteristic curve analysis for developing an automated grading system. The model's specificity, accuracy, precision, and sensitivity metrics crossed 92%, 86%, 82%, and 76%, respectively. 'Almost perfect agreement' with Fleiss kappa of 0.847 was obtained between automated and manual gradings. This pixel count-based grading of KOH images offers a novel, cost-effective solution for quantifying fungal load.

Bridging the pain gap after cancer surgery - Evaluating the feasibility of transitional pain service to prevent persistent postsurgical pain - A systematic review and meta-analysis

Background and Aims

The lack of a dedicated pain service catering to the postsurgical period has resulted in the origination of the pain-period gap. This has led to a resurgence of transitional pain service (TPS). Our objective was to evaluate the feasibility of TPS in pain practice among postsurgical cancer patients and its prevention of persistent postsurgical pain (PPSP), culminating in chronic pain catastrophising.

Methods

The protocol for this meta-analysis was registered in the International Prospective Register of Systematic Reviews (ID: CRD42023407190). This systematic review included articles involving all adult cancer patients undergoing cancer-related surgery experiencing pain, involving pharmacological, non-pharmacological and interventional pain modalities after an initial systematic pain assessment by pain care providers across diverse clinical specialities, targeting multimodal integrative pain management. Meta-analysis with meta-regression was conducted to analyse the feasibility of TPS with individual subgroup analysis and its relation to pain-related patient outcomes.

Results

Three hundred seventy-four articles were evaluated, of which 14 manuscripts were included in the meta-analysis. The lack of randomised controlled trials evaluating the efficacy of TPS in preventing PPSP and pain catastrophising led to the analysis of its feasibility by meta-regression. The estimate among study variances τ2 was determined and carried out along with multivariate subgroup analysis. A regression coefficient was attained to establish the correlation between the feasibility of TPS and its patient outcome measures and opioid-sparing.

Conclusion

TPS interventions carried out by multidisciplinary teams incorporating bio-physical-psychological pain interventions have resulted in its successful implementation with improved pain-related patient outcomes mitigating the occurrence of PPSP.

Tooth Loss in Periodontitis Patients-A Risk Factor for Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

BACKGROUND

Periodontal disease and tooth loss have been long suggested as risk factors of mild cognitive impairment. The underlying mechanisms could be systemic chronic inflammatory mediators, direct pathologic challenge to the nervous system, malnutrition and/or loss of neurosensory stimulation input causing brain atrophy. This review aimed to examine the existing literature studies linking the effect of periodontal disease and tooth loss on the development of mild cognitive impairment.

METHODS

A systematic review using PEO was conducted. Three electronic databases, namely Embase, Medline and DOSS (UCLan), were searched for relevant articles published up to April 2023. Google Scholar and a hand search were also conducted to ensure no relevant studies had been missed. The Newcastle-Ottawa scale was used to assess the quality of studies.

RESULTS

The findings showed that chronic periodontitis and tooth loss, both individually and in combination, led to an increased risk of mild cognitive decline in adults over 50 years. Within the limitations of this review, periodontitis and tooth loss both contribute to an increased risk of mild cognitive impairment and dementia, but the evidence so far is not strong.

CONCLUSIONS

In future, more robustly designed studies investigating periodontal disease and tooth losslink with cognitive health decline are required with a longer follow-up duration.

Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations

BACKGROUND

The application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle's milk production into low ([Formula: see text]4500 kg), medium (4500-7500 kg), and high ([Formula: see text]7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).

RESULTS

The comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA's 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.

CONCLUSION

The findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Artificial Intelligence in Coronary Artery Calcium Scoring

Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.

Social Media Promotion of Health Tests With Potential for Overdiagnosis or Overuse: Protocol for a Content Analysis

BACKGROUND

In recent years, social media have emerged as important spaces for commercial marketing of health tests, which can be used for the screening and diagnosis of otherwise generally healthy people. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest.

OBJECTIVE

This study aims to understand and quantify how social media is being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people.

METHODS

Content analysis of social media posts on the anti-Mullerian hormone test, whole-body magnetic resonance imaging scan, multicancer early detection, testosterone test, and gut microbe test from influential international social media accounts on Instagram and TikTok. The 5 tests have been identified as having the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. English language text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts are included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded. Using keywords related to the test, the top posts were searched and screened until there were 100 eligible posts from each platform for each test (total of 1000 posts). Data from the caption, video, and on-screen text are being summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take a combined inductive approach when generating key themes and a deductive approach using a prespecified framework. Quantitative data will be analyzed in Stata SE (version 18.0; Stata Corp).

RESULTS

Data on Instagram and TikTok have been searched and screened. Analysis has now commenced. The findings will be disseminated via publications in peer-reviewed international medical journals and will also be presented at national and international conferences in late 2024 and 2025.

CONCLUSIONS

This study will contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. This understanding is essential to develop strategies to mitigate potential harm and plan solutions, with the aim of helping to protect members of the public from being marketed low-value tests, becoming patients unnecessarily, and taking resources away from genuine needs within the health system.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56899.

Statistics for the Non-Statistician: How to Read the Respiratory Care Literature

Statistical analysis is an important part of the research process. Researchers are advised to include a statistician from the moment that the study is being planned. The statistical plan informs the research process, including sample size requirements and the most robust data collection. Once the data are collected, descriptive and inferential statistical analyses are performed. The results of this analysis determine whether the findings are significant, which leads to an interpretation of the findings. The importance of the statistical plan and analysis for the researcher is self-evident. However, it is also important for the reader of published papers to have some knowledge of statistical analysis. This allows critical review of all aspects of the published manuscript. The intent of this paper is to review some basic statistical concepts and thus allow the reader to become a better consumer of the literature.

A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Examining interrater agreement between self-report and proxy-report responses for the quality of life-aged care consumers (QOL-ACC) instrument

BACKGROUND

Quality of life is an important quality indicator for health and aged care sectors. However, self-reporting of quality of life is not always possible given the relatively high prevalence of cognitive impairment amongst older people, hence proxy reporting is often utilised as the default option. Internationally, there is little evidence on the impact of proxy perspective on interrater agreement between self and proxy report.

OBJECTIVES

To assess the impacts of (i) cognition level and (ii) proxy perspective on interrater agreement using a utility instrument, the Quality of Life-Aged Care Consumers (QOL-ACC).

METHODS

A cross-sectional study was undertaken with aged care residents and family member proxies. Residents completed the self-report QOL-ACC, while proxies completed two proxy versions: proxy-proxy perspective (their own opinion), and proxy-person perspective (how they believe the resident would respond). Interrater agreement was assessed using quadratic weighted kappas for dimension-level data and concordance correlation coefficients and Bland-Altman plots for utility scores.

RESULTS

Sixty-three residents (22, no cognitive impairment; 41, mild-to-moderate cognitive impairment) and proxies participated. In the full sample and in the mild-to-moderate impairment group, the mean self-reported QOL-ACC utility score was significantly higher than the means reported by proxies, regardless of perspective (p < 0.01). Agreement with self-reported QOL-ACC utility scores was higher when proxies adopted a proxy-person perspective.

CONCLUSION

Regardless of cognition level and proxy perspective, proxies tend to rate quality of life lower than residents. Further research is needed to explore the impact of such divergences for quality assessment and economic evaluation in aged care.

Rapid visual nucleic acid detection of Vibrio alginolyticus by recombinase polymerase amplification combined with CRISPR/Cas13a

Vibrio alginolyticus (V. alginolyticus) is a common pathogen in the ocean. In addition to causing serious economic losses in aquaculture, it can also infect humans. The rapid detection of nucleic acids of V. alginolyticus with high sensitivity and specificity in the field is very important for the diagnosis and treatment of infection caused by V. alginolyticus. Here, we established a simple, fast and effective molecular method for the identification of V. alginolyticus that does not rely on expensive instruments and professionals. The method integrates recombinase polymerase amplification (RPA) technology with CRISPR system in a single PCR tube. Using this method, the results can be visualized by lateral flow dipstick (LFD) in less than 50 min, we named this method RPA-CRISPR/Cas13a-LFD. The method was confirmed to achieve high specificity for the detection of V. alginolyticus with no cross-reactivity with similar Vibrio and common clinical pathogens. This diagnostic method shows high sensitivity; the detection limit of the RPA-CRISPR/Cas13a-LFD is 10 copies/µL. We successfully identified 35 V. alginolyticus strains from a total of 55 different bacterial isolates and confirmed their identity by (Matrix-assisted laser desorption ionization time-of-flight mass spectrometry, MALDI-TOF MS). We also applied this method on infected mice blood, and the results were both easily and rapidly obtained. In conclusion, RPA-CRISPR/Cas13a-LFD offers great potential as a useful tool for reliable and rapid diagnosis of V. alginolyticus infection, especially in limited conditions.