Risk factors of early spontaneous preterm birth despite carrying a cervical pessary in singleton pregnancies with a short cervix: Development of a risk prediction model

Introduction

We aimed to identify the incidence and risk factors of spontaneous preterm birth in pessary carriers with singleton pregnancies and a short cervix in the mid-trimester of pregnancy.

Material and Methods

Patient data were obtained from the PECEP Trial. We analyzed singleton pregnancies in pessary carriers with a short cervix (≤25 mm) between 18 and 22 gestational weeks. Demographics and obstetric history were compared to identify risk factors for spontaneous preterm birth < 34 gestational weeks. Each demographic and obstetric variable was compared between spontaneous preterm birth < 34 and ≥ 34 weeks of gestation.Regression analysis was used to identify risk factors. A risk score model was generated using the odds ratio for significant factors. The risk score model and spontaneous preterm birth risk were assessed using the receiver operating characteristic curve. Perinatal outcomes were compared by risk score.

Results

Among 190 pregnant individuals, 12 (6.3%) had spontaneous preterm birth < 34 gestational weeks. In the bivariate analysis, statistically significant differences between those with and without spontaneous preterm birth were only observed for mean cervical length at diagnosis and mean cervical length after pessary placement. By multiple logistic regression analysis, maternal age (OR 0.818; 95% CI 0.69-0.97; P 0.020), cervical length at diagnosis (OR 0.560; 95% CI 0.43-0.73; P < 0.001) and smoking status (OR 7.276; 95% CI 1.02-51.80; P 0.048) remained significantly associated with spontaneous preterm birth.The ROC curve from the multiple logistic regression analysis, including cervical length, maternal age and smoking status, had an area under the curve (AUC) of 0.952 (P < 0.001). The ROC curve for the risk score model incorporating all three variables had an AUC of 0.864 (95% CI 0.77-0.96; P < 0.001). A high-risk score was predictive of spontaneous preterm birth with a sensitivity of 75%, specificity of 84%, positive predictive value of 24%, and negative predictive value of 98%.Women with a high-risk score had a significantly reduced latency to delivery and poorer neonatal outcomes than those with a low-risk score.

Conclusions

Patients at a high risk for spontaneous preterm birth despite pessary therapy may be identified using cervical length at diagnosis added to maternal age and smoking status.

A machine learning model to assess potential misdiagnosed dengue hospitalization

Dengue, like other arboviruses with broad clinical spectra, can easily be misdiagnosed as other infectious diseases due to the overlap of signs and symptoms. During large outbreaks, severe dengue cases have the potential to overwhelm the health care system and understanding the burden of dengue hospitalizations is therefore important to better allocate medical care and public health resources. A machine learning model that used data from the Brazilian public healthcare system database and the National Institute of Meteorology (INMET) was developed to estimate potential misdiagnosed dengue hospitalizations in Brazil. The data was modeled into a hospitalization level linked dataset. Then, Random Forest, Logistic Regression and Support Vector Machine algorithms were assessed. The algorithms were trained by dividing the dataset in training/test set and performing a cross validation to select the best hyperparameters in each algorithm tested. The evaluation was done based on accuracy, precision, recall, F1 score, sensitivity, and specificity. The best model developed was Random Forest with an accuracy of 85% on the final reviewed test. This model shows that 3.4% (13,608) of all hospitalizations in the public healthcare system from 2014 to 2020 could have been dengue misdiagnosed as other diseases. The model was helpful in finding potentially misdiagnosed dengue and might be a useful tool to help public health decision makers in planning resource allocation.

Interaction of phonological biases and frequency in learning a probabilistic language pattern

We examine how learning a phonological rule in an artificial language interacts with morphological and lexical learning. We exposed adult participants to an artificial language in which noun plurals were marked by one of two prefix forms (ba- or ni-), one of which also triggered a velar palatalization rule (e.g., singular kimu, plural ni-chimu). In some conditions, the rule additionally created homophony. We also manipulated the relative frequency of the two prefix variants. The results showed that participants shifted away from using the rule-triggering prefix (ni-), but only when it was already the less frequent prefix. We attribute this effect to a paradigm uniformity bias leading participants to avoid phonological alternations (particularly in the stem). When the rule created homophony between lexical items, participants were less able to learn the rule, but it did not affect their choice of prefix. We attribute this effect to homophony avoidance interfering with participants' ability to extract the phonological generalization.

Space, feature, and risk sensitivity in homing pigeons (Columba livia): Broadening the conversation on the role of the avian hippocampus in memory

David Sherry has been a pioneer in investigating the avian hippocampal formation (HF) and spatial memory. Following on his work and observations that HF is sensitive to the occurrence of reward (food), we were interested in carrying out an exploratory study to investigate possible HF involvement in the representation goal value and risk. Control sham-lesioned and hippocampal-lesioned pigeons were trained in an open field to locate one food bowl containing a constant two food pellets on all trials, and two variable bowls with one containing five pellets on 75% (High Variable) and another on 25% (Low Variable) of their respective trials (High-Variable and Low-Variable bowls were never presented together). One pairing of pigeons learned bowl locations (space); another bowl colors (feature). Trained to color, hippocampal-lesioned pigeons performed as rational agents in their bowl choices and were indistinguishable from the control pigeons, a result consistent with HF regarded as unimportant for non-spatial memory. By contrast, when trained to location, hippocampal-lesioned pigeons differed from the control pigeons. They made more first-choice errors to bowls that never contained food, consistent with a role of HF in spatial memory. Intriguingly, the hippocampal-lesioned pigeons also made fewer first choices to both variable bowls, suggesting that hippocampal lesions resulted in the pigeons becoming more risk averse. Acknowledging that the results are preliminary and further research is needed, the data nonetheless support the general hypothesis that HF-dependent memory representations of space capture properties of reward value and risk, properties that contribute to decision making when confronted with a choice.

The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach

The ability to learn cause-effect relations from experience is critical for humans to behave adaptively - to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause-effect relations over days and weeks, which necessitates long-term memory. 413 participants completed a smartphone study, which compared learning a cause-effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks.

Predictive modeling of nontuberculous mycobacterial pulmonary disease epidemiology using German health claims data

OBJECTIVES

Administrative claims data are prone to underestimate the burden of non-tuberculous mycobacterial pulmonary disease (NTM-PD).

METHODS

We developed machine learning-based algorithms using historical claims data from cases with NTM-PD to predict patients with a high probability of having previously undiagnosed NTM-PD and to assess actual prevalence and incidence. Adults with incident NTM-PD were classified from a representative 5% sample of the German population covered by statutory health insurance during 2011-2016 by the International Classification of Diseases, 10th revision code A31.0. Pre-diagnosis characteristics (patient demographics, comorbidities, diagnostic and therapeutic procedures, and medications) were extracted and compared to that of a control group without NTM-PD to identify risk factors.

RESULTS

Applying a random forest model (area under the curve 0.847; total error 19.4%) and a risk threshold of >99%, prevalence and incidence rates in 2016 increased 5-fold and 9-fold to 19 and 15 cases/100,000 population, respectively, for both coded and non-coded vs. coded cases alone.

CONCLUSIONS

The use of a machine learning-based algorithm applied to German statutory health insurance claims data predicted a considerable number of previously unreported NTM-PD cases with high probabilty.

Human free-operant performance varies with a concurrent task: Probability learning without a task, and schedule-consistent with a task

Three experiments examined human rates and patterns of responding during exposure to various schedules of reinforcement with or without a concurrent task. In the presence of the concurrent task, performances were similar to those typically noted for nonhumans. Overall response rates were higher on medium-sized ratio schedules than on smaller or larger ratio schedules (Experiment 1), on interval schedules with shorter than longer values (Experiment 2), and on ratio compared with interval schedules with the same rate of reinforcement (Experiment 3). Moreover, bout-initiation responses were more susceptible to influence by rates of reinforcement than were within-bout responses across all experiments. In contrast, in the absence of a concurrent task, human schedule performance did not always display characteristics of nonhuman performance, but tended to be related to the relationship between rates of responding and reinforcement (feedback function), irrespective of the schedule of reinforcement employed. This was also true of within-bout responding, but not bout-initiations, which were not affected by the presence of a concurrent task. These data suggest the existence of two strategies for human responding on free-operant schedules, relatively mechanistic ones that apply to bout-initiation, and relatively explicit ones, that tend to apply to within-bout responding, and dominate human performance when other demands are not made on resources.

Learning reward frequency over reward probability: A tale of two learning rules

Learning about the expected value of choice alternatives associated with reward is critical for adaptive behavior. Although human choice preferences are affected by the presentation frequency of reward-related alternatives, this may not be captured by some dominant models of value learning, such as the delta rule. In this study, we examined whether reward learning is driven more by learning the probability of reward provided by each option, or how frequently each option has been rewarded, and assess how well models based on average reward (e.g. the delta model) and models based on cumulative reward (e.g. the decay model) can account for choice preferences. In a binary-outcome choice task, participants selected between pairs of options that had reward probabilities of 0.65 (A) versus 0.35 (B) or 0.75 (C) versus 0.25 (D). Crucially, during training there were twice the number of AB trials as CD trials, such that option A was associated with higher cumulative reward, while option C gave higher average reward. Participants then decided between novel combinations of options (e.g., AC). Most participants preferred option A over C, a result predicted by the Decay model, but not the Delta model. We also compared the Delta and Decay models to both more simplified as well as more complex models that assumed additional mechanisms, such as representation of uncertainty. Overall, models that assume learning about cumulative reward provided the best account of the data.

Probability Learning in an Uncertain World: How Children Adjust to Changing Contingencies

We regularly make predictions about future events, even in a world where events occur probabilistically rather than deterministically. Our environment may even be non-stationary such that the probability of an event may change suddenly or from one context to another. 4-6 year olds and adults viewed 3 boxes and guessed the location of a hidden toy. After 80 trials with one set of probabilities assigned to the 3 boxes, the spatial distribution of these probabilities was altered. Adults easily responded to this change, with participants who maximized in the first half (by choosing the most common location at a higher rate than it was presented) being the fastest at making this shift. Only the older children successfully switched to the new location, with younger children either partially switching, perseverating on their original strategy, or failing to learn the first distribution, suggesting a fundamental development in children's response to changing probabilities.

Learning from non-representative instances: Children's sample and population predictions

What do children learn from biased samples? Most samples people encounter are biased in some way, and responses to bias can distinguish among different theories of inductive inference. A sample of 67 4- to 8-year-old children learned to make conditional predictions about a set of sample items. They then made predictions about the properties of new instances or old instances from the training set. The experiment compared unbiased and biased sampling. Given unbiased samples, participants used what they learned to make predictions about population and sample instances. With biased samples, children were less accurate/confident about inferences about the population than about the sample. Children used information in a biased sample to make predictions about items in that sample, but they were less likely to generalize to new items than when samples were unbiased.

[Two-and-a-half year follow-up study of strategy factors in successful learning to predict academic achievements in medical education]

PURPOSE

We performed a two-and-a-half year follow-up study of strategy factors in successful learning to predict academic achievements in medical education.

METHODS

Strategy factors in successful learning were identified using a content analysis of open-ended responses from 30 medical students who were ranked in the top 10 of their class. Core words were selected among their responses in each category and the frequency of the words were counted. Then, a factors survey was conducted among year 2 students, before the second semester. Finally, we performed an analysis to assess the association between the factors score and academic achievement for the same students 2.5 years later.

RESULTS

The core words were "planning and execution," "daily reviews" in the study schedule category; "focusing in class" and "taking notes" among class-related category; and "lecture notes," "previous exams or papers," and "textbooks" in the primary self-learning resources category. There were associations between the factors scores for study planning and execution, focusing in class, and taking notes and academic achievement, representing the second year second semester credit score, third year written exam scores and fourth year written and skill exam scores. Study planning was only one independent variable to predict fourth year summative written exam scores.

CONCLUSION

In a two-and-a-half year follow-up study, associations were founded between academic achievement and the factors scores for study planning and execution, focusing in class, and taking notes. Study planning as only one independent variable is useful for predicting fourth year summative written exam score.