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This paper replies to Politzer’s () criticisms of the mental model theory of conditionals. It argues that the theory provides a correct account of negation of conditionals, that it does not provide a truth-functional account of their meaning, though it predicts that certain interpretations of conditionals yield acceptable versions of the ‘paradoxes’ of material implication, and that it postulates three main strategies for estimating the probabilities of conditionals. [ABSTRACT FROM AUTHOR]

This study investigated two main issues: Whether people's judgments about real-life moral transgressions are affected by the role they play in them and whether self-serving biases in such judgments vary with level of moral development. One-hundred twenty university students took Colby and Kohlberg's (1987) test of moral judgment and made open-ended and rating-scale judgments about three real-life transgressions they considered moral in nature. As expected, participants made more exculpatory judgments about transgressions they committed than they did about transgressions others committed, but participants did not judge transgressions committed against them more harshly than they judged transgressions committed against others. The higher participants scored on Kohlberg's test the less they externalized and excused their moral transgressions. Contrary to expectation, this relation also applied to moral judgments about transgressions committed by others against others. These findings have important implications for models of moral development and social cognition. [ABSTRACT FROM AUTHOR]

The article presents a case study of a seven-year old African-American girl who have reduction in vision in the right eye on routine screening. The patient underwent examination and was diagnosed with ciliary body medulloepithelioma associated with pleuropulmonary blastoma. She then underwent chemotheraphy, surgical resection of the tumour, and was in remission at the time of vision loss.

Abstract: This article presents a fundamental advance in the theory of mental models as an explanation of reasoning about facts, possibilities, and probabilities. It postulates that the meanings of compound assertions, such as conditionals (if) and disjunctions (or), unlike those in logic, refer to conjunctions of epistemic possibilities that hold in default of information to the contrary. Various factors such as general knowledge can modulate these interpretations. New information can always override sentential inferences; that is, reasoning in daily life is defeasible (or nonmonotonic). The theory is a dual process one: It distinguishes between intuitive inferences (based on system 1) and deliberative inferences (based on system 2). The article describes a computer implementation of the theory, including its two systems of reasoning, and it shows how the program simulates crucial predictions that evidence corroborates. It concludes with a discussion of how the theory contrasts with those based on logic or on probabilities. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the intensive care unit and is associated with increased morbidity and mortality. New-onset atrial fibrillation (NOAF) is often initially paroxysmal and fleeting, making it difficult to diagnose, and therefore difficult to understand the true burden of disease. Automated algorithms to detect AF in the ICU have been advocated as a means to better quantify its true burden. Results: We used a publicly available 12-lead ECG dataset to train a deep learning model for the classification of AF. We then conducted an external independent validation of the model using continuous telemetry data from 984 critically ill patients collected in our institutional database. Performance metrics were stratified by signal quality, classified as either clean or noisy. The deep learning model was able to classify AF with an overall sensitivity of 84%, specificity of 89%, positive predictive value (PPV) of 55%, and negative predictive value of 97%. Performance was improved in clean data as compared to noisy data, most notably with respect to PPV and specificity. Conclusions: This model demonstrates that computational detection of AF is currently feasible and effective. This approach stands to improve the efficiency of retrospective and prospective research into AF in the ICU by automating AF detection, and enabling precise quantification of overall AF burden. [ABSTRACT FROM AUTHOR]

This paper presents an investigation testing the idea that personality affects the way people reason in a predictable way. That is to say, traits may affect the particular possibilities that individuals envisage and so, in turn, how they reason. Different traits can elicit different ways of thinking and as a result, individuals would reason better in the fields that are pertinent to their personality. We tested 94 participants with the NEO-PI-R and with a conditional inference task that was tailored based on the items in the NEO-PI-R personality test. We found, in general, that our participants reasoned better (higher rate of Modus Tollens) with the materials that were related to the personality trait in which they scored highest. Also, the participants who scored high in Extraversion or Neuroticism produced more valid inferences for the items related to their personality than those who scored low in the same trait. These results are in accordance with the principle of inferential consequences and the Mental Models Theory of Reasoning. [ABSTRACT FROM AUTHOR]

The vast quantities of data generated and collected in the Intensive Care Unit (ICU) have given rise to large retrospective datasets that are frequently used for observational studies. The temporal nature and fine granularity of much of the data collected in the ICU enable the pursuit of predictive modeling. In particular, forecasting acute hypotensive episodes (AHE) in intensive care patients has been of interest to researchers in critical care medicine. Given an advance warning of an AHE, care providers may be prompted to search for evolving disease processes and help mitigate negative clinical outcomes. However, the conventionally adopted definition of an AHE does not account for inter-patient variability and is restrictive. To reflect the wider trend of global clinical and research efforts in precision medicine, we introduce a patient-specific definition of AHE in this study and propose deep learning based models to predict this novel definition of AHE in data from multiple independent institutions. We provide extensive evaluation of the models by studying their accuracies in detecting patient-specific AHEs with lead-times ranging from 10 min to 1 hour before the onset of the event. The resulting models achieve AUROC values ranging from 0.57–0.87 depending on the lead time of the prediction. We demonstrate the generalizability and robustness of our approach through the use of independent multi-institutional data. [ABSTRACT FROM AUTHOR]

Cognitive scientists treat verification as a computation in which descriptions that match the relevant situation are true, but otherwise false. The claim is controversial: The logician Gödel and the physicist Penrose have argued that human verifications are not computable. In contrast, the theory of mental models treats verification as computable, but the two truth values of standard logics, true and false , as insufficient. Three online experiments (n = 208) examined participants' verifications of disjunctive assertions about a location of an individual or a journey, such as: 'You arrived at Exeter or Perth'. The results showed that their verifications depended on observation of a match with one of the locations but also on the status of other locations (Experiment 1). Likewise, when they reached one destination and the alternative one was impossible, their use of the truth value: could be true and could be false increased (Experiment 2). And, when they reached one destination and the only alternative one was possible, they used the truth value, true and it couldn't have been false , and when the alternative one was impossible, they used the truth value: true but it could have been false (Experiment 3). These truth values and those for falsity embody counterfactuals. We implemented a computer program that constructs models of disjunctions, represents possible destinations, and verifies the disjunctions using the truth values in our experiments. Whether an awareness of a verification's outcome is computable remains an open question.

Atrial fibrillation (AF) is the most common arrhythmia found in the intensive care unit (ICU), and is associated with many adverse outcomes. Effective handling of AF and similar arrhythmias is a vital part of modern critical care, but obtaining knowledge about both disease burden and effective interventions often requires costly clinical trials. A wealth of continuous, high frequency physiological data such as the waveforms derived from electrocardiogram telemetry are promising sources for enriching clinical research. Automated detection using machine learning and in particular deep learning has been explored as a solution for processing these data. However, a lack of labels, increased presence of noise, and inability to assess the quality and trustworthiness of many machine learning model predictions pose challenges to interpretation. In this work, we propose an approach for training deep AF models on limited, noisy data and report uncertainty in their predictions. Using techniques from the fields of weakly supervised learning, we leverage a surrogate model trained on non-ICU data to create imperfect labels for a large ICU telemetry dataset. We combine these weak labels with techniques to estimate model uncertainty without the need for extensive human data annotation. AF detection models trained using this process demonstrated higher classification performance (0.64-0.67 F1 score) and improved calibration (0.05-0.07 expected calibration error)., (© 2022. The Author(s).)

Objective. To develop a standardized format for exchanging clinical and physiologic data generated in the intensive care unit. Our goal was to develop a format that would accommodate the data collection pipelines of various sites but would not require dataset-specific schemas or ad-hoc tools for decoding and analysis. Approach. A number of centers had independently developed solutions for storing clinical and physiologic data using Hierarchical Data Format-Version 5 (HDF5), a well-supported standard already in use in multiple other fields. These individual solutions involved design choices that made the data difficult to share despite the underlying common framework. A collaborative process was used to form the basis of a proposed standard that would allow for interoperability and data sharing with common analysis tools. Main Results. We developed the HDF5-based critical care data exchange format which stores multiparametric data in an efficient, self-describing, hierarchical structure and supports real-time streaming and compression. In addition to cardiorespiratory and laboratory data, the format can, in future, accommodate other large datasets such as imaging and genomics. We demonstated the feasibility of a standardized format by converting data from three sites as well as the MIMIC III dataset. Significance. Individual approaches to the storage of multiparametric clinical data are proliferating, representing both a duplication of effort and a missed opportunity for collaboration. Adoption of a standardized format for clinical data exchange will enable the development of a digital biobank, facilitate the external validation of machine learning models and be a powerful tool for sharing multiparametric, high frequency patient level data in multisite clinical trials. Our proposed solution focuses on supporting standardized ontologies such as LOINC allowing easy reading of data regardless of the source and in so doing provides a useful method to integrate large amounts of existing data., (© 2021 Institute of Physics and Engineering in Medicine.)

Background: The cases discussed highlight the atypical presentation and diagnostic dilemmas of toxoplasmosis with fulminant retinal necrosis and the potentially devastating visual outcomes of toxoplasma chorioretinitis following local corticosteroid exposure., Case Presentation: We report a series of three patients who presented with toxoplasmosis mimicking severe acute retinal necrosis. Patients were between 59 and 77 years old and had been exposed to local corticosteroids preceding our evaluation. All patients demonstrated diffuse retinal whitening with severe vision loss on presentation. Polymerase chain reaction testing (PCR) was diagnostic in two patients, and histopathologic examination of a vitrectomy specimen was diagnostic in one patient. All cases of retinitis resolved with anti-parasitic medication; however, visual acuity failed to improve in all patients due to disease severity and presentation., Conclusions: Local corticosteroid injection may trigger or exacerbate toxoplasmosis chorioretinitis, leading to fulminant retinal necrosis and severe vision loss. Toxoplasma chorioretinitis should be considered in the differential diagnosis of patients presenting with clinical features of acute retinal necrosis, particularly following local corticosteroid injection regardless of their baseline systemic immune status. Diagnostic vitrectomy may be helpful in patients in whom PCR testing is negative and ocular toxoplasmosis is suspected., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Forecasting acute hypotensive episodes (AHE) in intensive care patients has been of recent interest to researchers in the healthcare domain. Advance warning of an impending AHE may give care providers additional information to help mitigate the negative clinical impact of a serious event such as an AHE or prompt a search for an evolving disease process. However, the currently accepted definition of AHE is restrictive does not account for inter-patient variability. In this paper, we propose a novel definition of an AHE based on patient-specific features of blood pressure recordings. Next, we utilize a deep learning-based method to predict the onset of an AHE from multiple physiological readings for different definitions of the prediction task including variable input and gap lengths. Using a cohort of 538 patients, our model was able to successfully predict the onset of an AHE with an accuracy and AUC score of 0.80 and 0.87 respectively. Compared to a baseline logistic regression model, our model outperforms the baseline in most of the definitions of the prediction task.

The vast quantities of data generated and collected in the Intensive Care Unit (ICU) have given rise to large retrospective datasets that are frequently used for observational studies. The temporal nature and fine granularity of much of the data collected in the ICU enable the pursuit of predictive modeling, an increasingly common topic in ICU literature. Since patient conditions can rapidly change in the ICU, predicting the onset of events that are indicative of deteriorating patient state has potential clinical utility. However, the development of predictive modeling applications using ICU data requires a number of considerations to maximize prospective performance and clinical utility. In this study, we discuss the challenges encountered and considerations taken by using the prediction of acute hypotensive episodes as an example.

Purpose: To evaluate postoperative pain level using a supplemental peribulbar injection at the conclusion of retinal surgery., Design: Prospective, parallel-assigned, single-masked, randomized clinical trial., Participants: Fifty-eight patients undergoing scleral buckle, vitrectomy, or combined surgery., Methods: In a single academic institutional practice, 58 patients undergoing scleral buckle, vitrectomy, or combined surgery were enrolled. Exclusion criteria included those with a risk for glaucoma, a pre-existing chronic pain disorder, among others. Patients were assigned randomly to receive a postoperative peribulbar formulation of either bupivacaine, triamcinolone acetonide, and cefazolin (group A) or bupivacaine, balanced salt solution, and cefazolin (group B). The postoperative pain score and ocular motility were assessed by a masked observer on the first postoperative day., Main Outcome Measures: The primary outcome measure was the postoperative pain score. Secondary outcome measures included oral analgesic use, ocular motility, and intraocular pressure (IOP)., Results: The mean pain scores were 2.8±2.9 for group A and 3.8±2.6 for group B (P = 0.095). Pain was absent in 28% of group A patients versus 14% of group B patients (P = 0.11). Group A required less narcotic pain medication (hydroxycodone: group A, 0.7±3 mg vs. group B, 3±6 mg; P = 0.05; oxycodone: group A, 7±7 mg vs. 9±13 mg; P = 0.2) than group B. Motility was full in group B and limited in group A (P ≤ 0.001), with no differences in mean IOP measurements at any point after surgery., Conclusions: We did not demonstrate a statistically significant reduction in mean postoperative pain scores. However, patients in group A required less hydroxycodone use and had greater akinesia, suggesting prolonged neural blockade., (Copyright © 2017 American Academy of Ophthalmology. All rights reserved.)

Ways of thinking: Personality affects reasoning. This paper presents an investigation testing the idea that personality affects the way people reason in a predictable way. That is to say, traits may affect the particular possibilities that individuals envisage and so, in turn, how they reason. Different traits can elicit different ways of thinking and as a result, individuals would reason better in the fields that are pertinent to their personality. We tested 94 participants with the NEO-PI-R and with a conditional inference task that was tailored based on the items in the NEO-PI-R personality test. We found, in general, that our participants reasoned better (higher rate of Modus Tollens) with the materials that were related to the personality trait in which they scored highest. Also, the participants who scored high in Extraversion or Neuroticism produced more valid inferences for the items related to their personality than those who scored low in the same trait. These results are in accordance with the principle of inferential consequences and the Mental Models Theory of Reasoning.

Purpose: In an initial study of prism-induced exodeviation, degraded stereoacuity was not associated with decreased binocular visual acuity, suggesting that accommodative convergence was not recruited. Distance stereoacuity degraded earlier when measured with the Frisby-Davis Distance (FD2) than when measured with the Distance Randot (DR). We now describe a follow-up study in which we reversed the prism order and also addressed potential biases of testing order and different measurable levels of stereoacuity to clarify the relationship between exodeviation, distance stereoacuity, and binocular visual acuity., Methods: Convergence stress was induced with base-out prism in 20 adults and increased stepwise. Stereoacuity and binocular visual acuity were measured at each step. Disparities tested were 200, 100, and 60 arcsec, and testing was repeated at each prism step., Results: Most subjects showed degraded stereoacuity with the FD2 in contrast to the DR (80% vs 45%, p = 0.02). Reduction of stereoacuity occurred earlier on the FD2 than on the DR (median, 18(Delta) vs 40(Delta), p = 0.001). Degradation of stereoacuity was associated with minimal change in binocular visual acuity from baseline to maximal convergence stress (median, 20/15 to 20/20)., Conclusions: Convergence stress is associated with decreased distance stereoacuity that does not appear to be due to accommodative convergence. Performance on real depth stereotests (FD2) is affected more than random dot tests (DR), in contrast to previous findings in intermittent exotropia. There appear to be different mechanisms for decreased stereoacuity in intermittent exotropia and under conditions of convergence stress in nonstrabismic subjects.

Background: Intermittent exotropia may be controlled by accommodative or motor convergence. Previous studies have reported that reduced binocular visual acuity in intermittent exotropia is due to the accommodation required to control the deviation. To test this hypothesis, we simulated convergence stress by inducing exodeviations in normal volunteers to investigate whether the transition from nonfused to fused is associated with a gradual or immediate transition in stereoacuity and binocular visual acuity., Methods: Convergence stress was induced in 25 visually normal adults with 40(Delta) base-out prism and reduced stepwise in increments of 5(Delta) until 20(Delta), and 2(Delta) thereafter. Stereoacuity (Frisby Davis Distance [FD2] and the Distance Randot [DR]) and binocular visual acuity were measured at each step. For each subject, the recovery of fine stereoacuity (< or =60 arcsec) was categorized as immediate (nil to fine) or gradual (nil to moderate to fine)., Results: Twenty-four of 25 (96%) showed gradual recovery of fine stereoacuity on either FD2 or DR. Median binocular visual acuity at baseline, first level of fusion, and subsequent levels was 20/15., Conclusions: Under convergence stress, the transition from nonfused to fused is accompanied by a gradual recovery of fine stereoacuity in most individuals, consistent with some studies of patients with intermittent exotropia. Nevertheless, this degradation of stereoacuity was not associated with decreased binocular visual acuity, suggesting that accommodative convergence may not be recruited to restore and maintain binocularity under conditions of convergence stress.