Substance use disorders among armed forces veterans with gambling disorder: Insights from an inpatient treatment sample.

Armed forces veterans are especially prone to experiencing a variety of addictive disorders, including gambling disorder. Even so, gambling disorder in veterans remains understudied generally, and there remain significant gaps in the research literature with regards to how gambling disorder relates to the experience and expression of comorbid substance use disorders. The present work examines the prevalence, presentation, and clinical associations of substance use disorders in U.S. Armed Forces veterans receiving inpatient treatment for gambling disorder. Participants (N = 664) were veterans from all branches of the U.S. Armed Forces receiving inpatient treatment for gambling disorder through the Department of Veteran Affairs Healthcare System. Clinical data from the time of intake was analyzed. A substantial portion of veterans (36.1%) met current criteria for an SUD, with another 16.5% reporting a history of SUD. Alcohol use disorder was the most commonly reported SUD (76.1% of those with a current SUD), with polysubstance use disorders and stimulant use disorders each occurring in at least 25% of those with SUDs. SUD status was related to greater levels of impulsivity, but there was no evidence that SUD status was related to gambling symptom severity, gambling preferences, or further psychiatric comorbidities.

Epigenetic developmental mechanisms underlying sex differences in cancer.

Cancer risk is modulated by hereditary and somatic mutations, exposures, age, sex, and gender. The mechanisms by which sex and gender work alone and in combination with other cancer risk factors remain underexplored. In general, cancers that occur in both the male and female sexes occur more commonly in XY compared with XX individuals, regardless of genetic ancestry, geographic location, and age. Moreover, XY individuals are less frequently cured of their cancers, highlighting the need for a greater understanding of sex and gender effects in oncology. This will be necessary for optimal laboratory and clinical cancer investigations. To that end, we review the epigenetics of sexual differentiation and its effect on cancer hallmark pathways throughout life. Specifically, we will touch on how sex differences in metabolism, immunity, pluripotency, and tumor suppressor functions are patterned through the epigenetic effects of imprinting, sex chromosome complement, X inactivation, genes escaping X inactivation, sex hormones, and life history.

Chronic Opioid Prescribing After Common Otolaryngology Procedures in Adults.

OBJECTIVE: (1) Describe short and long-term opioid prescribing patterns and variation after common otolaryngologic procedures and (2) assess risk factors for chronic opioid use in this cohort. STUDY DESIGN: Retrospective cohort. SETTING: Optum's deidentified Integrated Claims-Clinical data set. METHODS: An adult cohort of patients undergoing common otolaryngology procedures from 2010 to 2017 was identified. Associations between procedure and other covariates with any initial opioid prescription and continuous opioid prescriptions were assessed with multivariable modeling. Opioid use was defined as continuous if a new prescription was filled within 30 days of the previous prescription. A time-to-event analysis assessed continuous prescriptions from the index procedure to end of the last continuous opioid prescription. RESULTS: Among a cohort of 19,819 patients undergoing predominately laryngoscopy procedures (12,721, 64.2%), 2585 (13.0%) received an opioid prescription with variation in receiving a prescription, daily dose, and total initially prescribed dose varying by procedure, patient demographics, provider characteristics, and facility type. Opioids were prescribed most frequently after tonsillectomy (45.4%) and least frequently after laryngoscopy with interventions (3.9%), which persisted in the multivariable models. Overall rates of continuous use at 180 and 360 days were 0.48% and 0.27%, respectively. Among patients receiving an initial opioid prescription, maintaining continuous prescriptions was associated with tonsillectomy procedures, age (adjusted hazard ratio [aHR]: 0.997 per year, 95% confidence interval [CI]: 0.993-0.999), opioid prescriptions 6 months preprocedure (aHR: 0.42, 95% CI: 0.37-0.47), and nonotolaryngology initial prescribers (aHRs: <1, P < .05). CONCLUSION: There is substantial variation in initial prescribing practices and continuous opioid prescriptions after common Otolaryngology procedures, but the overall rate of maintaining a continuous prescription starting after these procedures is very low. LEVEL OF EVIDENCE: Level 3.

Characterization of adverse events in injured patients at risk of hemorrhagic shock: a secondary analysis of three harmonized prehospital randomized clinical trials.

Background: The reporting of adverse events (AEs) is required and well defined in the execution of clinical trials, but is poorly characterized particularly in prehospital trials focusing on traumatic injury. In the setting of prehospital traumatic injury trials, no literature currently exists analyzing the clinical implications of AEs and their associations with mortality and morbidity. We sought to analyze AEs from three prehospital hemorrhagic shock trials and characterize their time course, incidence, severity, associated clinical outcomes, and relatedness. Methods: We performed a secondary analysis of three prehospital randomized clinical trials. We analyzed AEs at both the patient level as well as the individual AE level. We categorized patients who had no AEs, a single documented AE and those with multiple events (>1 AE). We characterized AE timing, severity, relatedness and attributable mortality outcomes. Results: We included 1490 patients from the three harmonized clinical trials, with 299 (20.1%) individual patients having at least a single AE documented with 529 AEs documented overall as a proportion of patients had multiple events. Over 44% of patients had a death-related misclassified AE. Patients with at least a single documented AE had a significantly higher 28-day mortality (log-rank χ2=81.27, p<0.001) compared with those without an AE documented. Patients with a single AE had a significant higher mortality than those with multiple AEs, potentially due to survival bias (log-rank χ2=11.80, p=0.006). When relatedness of each individual AE was characterized, over 97% of AEs were classified as 'definitely not related' or 'probably not related' to the intervention. Conclusions: AEs in hemorrhagic shock trials are common, occur early and are associated with mortality and survival bias. The potential for inaccurate reporting exists, and education and training remain essential for appropriate treatment arm comparison. The current results have important relevance to injury-related clinical trials. Trial registration numbers: NCT01818427, NCT02086500 and NCT03477006. Level of evidence: II.

Synthesis and Cheminformatics-Directed Antibacterial Evaluation of Echinosulfonic Acid-Inspired Bis-Indole Alkaloids.

Synthetic efforts toward complex natural product (NP) scaffolds are useful ones, particularly those aimed at expanding their bioactive chemical space. Here, we utilised an orthogonal cheminformatics-based approach to predict the potential biological activities for a series of synthetic bis-indole alkaloids inspired by elusive sponge-derived NPs, echinosulfone A (1) and echinosulfonic acids A-D (2-5). Our work includes the first synthesis of desulfato-echinosulfonic acid C, an α-hydroxy bis(3'-indolyl) alkaloid (17), and its full NMR characterisation. This synthesis provides corroborating evidence for the structure revision of echinosulfonic acids A-C. Additionally, we demonstrate a robust synthetic strategy toward a diverse range of α-methine bis(3'-indolyl) acids and acetates (11-16) without the need for silica-based purification in either one or two steps. By integrating our synthetic library of bis-indoles with bioactivity data for 2048 marine indole alkaloids (reported up to the end of 2021), we analyzed their overlap with marine natural product chemical diversity. Notably, the C-6 dibrominated α-hydroxy bis(3'-indolyl) and α-methine bis(3'-indolyl) analogues (11, 14, and 17) were found to contain significant overlap with antibacterial C-6 dibrominated marine bis-indoles, guiding our biological evaluation. Validating the results of our cheminformatics analyses, the dibrominated α-methine bis(3'-indolyl) alkaloids (11, 12, 14, and 15) were found to exhibit antibacterial activities against methicillin-sensitive and -resistant Staphylococcus aureus. Further, while investigating other synthetic approaches toward bis-indole alkaloids, 16 incorrectly assigned synthetic α-hydroxy bis(3'-indolyl) alkaloids were identified. After careful analysis of their reported NMR data, and comparison with those obtained for the synthetic bis-indoles reported herein, all of the structures have been revised to α-methine bis(3'-indolyl) alkaloids.

Perfusion Measures and Outcomes (PERForm) registry: First annual report.

BACKGROUND: The Perfusion Measures and Outcomes (PERForm) registry was established in 2010 to advance cardiopulmonary bypass (CPB) practices and outcomes. The registry is maintained through the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative and is the official registry of the American Society of Extracorporeal Technology. METHODS: This first annual PERForm registry report summarizes patient characteristics as well as CPB-related practice patterns in adult (≥18 years of age) patients between 2019 and 2022 from 42 participating hospitals. Data from PERForm are probabilistically matched to institutional surgical registry data. Trends in myocardial protection, glucose, anticoagulation, temperature, anemia (hematocrit), and fluid management are summarized. Additionally, trends in equipment (hardware/disposables) utilization and employed patient safety practices are reported. RESULTS: A total of 40,777 adult patients undergoing CPB were matched to institutional surgical registry data from 42 hospitals. Among these patients, 54.9% underwent a CABG procedure, 71.6% were male, and the median (IQR) age was 66.0 [58.0, 73.0] years. Overall, 33.1% of the CPB procedures utilized a roller pump for the arterial pump device, and a perfusion checklist was employed 99.6% of the time. The use of conventional ultrafiltration decreased over the study period (2019 vs. 2022; 27.1% vs. 24.9%) while the median (IQR) last hematocrit on CPB has remained stable [27.0 (24.0, 30.0) vs. 27.0 (24.0, 30.0)]. Pump sucker termination before protamine administration increased over the study period: (54.8% vs. 75.9%). CONCLUSION: Few robust clinical registries exist to collect data regarding the practice of CPB. Although data submitted to the PERForm registry demonstrate overall compliance with published perfusion evidence-based guidelines, noted opportunities to advance patient safety and outcomes remain.

Evaluating language models for mathematics through interactions.

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of evaluating LLMs relies on static pairs of inputs and outputs; this is insufficient for making an informed decision about which LLMs are best to use in an interactive setting, and how that varies by setting. Static assessment therefore limits how we understand language model capabilities. We introduce CheckMate, an adaptable prototype platform for humans to interact with and evaluate LLMs. We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics, with a mixed cohort of participants from undergraduate students to professors of mathematics. We release the resulting interaction and rating dataset, MathConverse. By analyzing MathConverse, we derive a taxonomy of human query behaviors and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness in LLM generations, among other findings. Further, we garner a more granular understanding of GPT-4 mathematical problem-solving through a series of case studies, contributed by experienced mathematicians. We conclude with actionable takeaways for ML practitioners and mathematicians: models that communicate uncertainty, respond well to user corrections, and can provide a concise rationale for their recommendations, may constitute better assistants. Humans should inspect LLM output carefully given their current shortcomings and potential for surprising fallibility.

Developmental stage and level of submersion in water impact the viability of lone star and winter tick eggs during flooding.

Female ticks deposit large egg clusters that range in size from hundreds to thousands. These clusters are restricted to a deposition site, usually under leaf litter and other debris. These sites can be exposed to periodic flooding, where the cluster of tick eggs can float to the surface or remain underneath organic debris entirely underwater. Here, we examined the viability of egg clusters from winter ticks, Dermacentor albipictus , and lone star ticks, Amblyomma americanum , when partially submerged or fully submerged in water in relation to the developmental stages of the eggs. In general, egg clusters that were older and partially submerged had a higher viability than fully submerged, young eggs in water. A. americanum was much more resistant to water exposure between the two species. These studies highlight that egg clusters for specific tick species can remain viable when exposed to water for at least two weeks, where eggs float on the surface. These studies also suggest that water-based distribution of egg clusters could occur for some species, and flooding will differentially impact tick egg survival based on the specific developmental stage of exposure and species.

Autism Symptom Presentation and Hierarchical Models of Intelligence.

BACKGROUND: There is a substantial history studying the relationship between general intelligence and the core symptoms of autism. However, a gap in knowledge is how dimensional autism symptomatology associates with different components of clinically-relevant hierarchical models of intelligence. METHOD: We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence. One autistic cohort was tested on the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV; N = 131), and another on the fifth edition (WISC-V; N = 83). We anticipated a convergent pattern of results between cohorts. RESULTS: On WISC-IV, ADOS scores were correlated significantly with g and three out of four intermediate factor scores, which was a broader pattern of correlations than anticipated from the literature. In the WISC-V cohort, only one intermediate factor correlated significantly with the ADOS; correlations with g and the other intermediate factors were less statistically certain. ADOS-factor correlations were larger in the WISC-IV than WISC-V cohort; this difference was significant at the 90% level. CONCLUSIONS: WISC-IV shows dimensional relationships with ADOS at multiple points in the hierarchical model of intelligence. Moreover, the current results provide evidence that relationship between core autism symptomatology and the construct of general intelligence may depend on how intelligence is measured. Known cohort effects in the relationship between categorical autism diagnosis and general intelligence have previously been attributed to changes in autism diagnostic practices. To our knowledge, this is the first evidence that differing versions of IQ tests may be implicated.

Trauma-Induced Coagulopathy: Prevalence and Association with Mortality Persist Twenty Years Later.

INTRODUCTION: A 2003 landmark study identified the prevalence of eTIC at 28% with a strong association with mortality of 8.9%. Over the last 20 years there have been significant advances in both the fundamental understanding of eTIC and therapeutic interventions. METHODS: A retrospective cohort study was performed from 2018-2022 on patients ≥18 using prospectively collected data from two level 1 trauma centers and compared to data from 2003. Demographics, laboratory data and clinical outcomes were obtained. RESULTS: There were 20,107 patients meeting criteria: 65% male, 85% blunt, mean age 54 ± 21 years, median injury severity score (ISS) 10 [10, 18]), 8% of patients were hypotensive on arrival, with an all-cause mortality 6.0%. The prevalence of eTIC remained high at 32% in patients with an abnormal PT and 10% with an abnormal PTT, for an overall combined prevalence of 33.4%. Coagulopathy had a major impact on mortality over all injury severity ranges, with the greatest impact with lower ISS. In a hybrid logistic regression/Classification and Regression Trees analysis, coagulopathy was independently associated with a 2.1-fold increased risk of mortality (95% CI 1.5-2.9); the predictive quality of the model was excellent (AUROC 0.932). CONCLUSION: The presence of eTIC conferred a higher risk of death across all disease severities and was independently associated with a greater risk of death. Biomarkers of coagulopathy associated with eTIC remain strongly predictive of poor outcome despite advances in trauma care.

Firearm Policy in the Hospital Setting-Recognizing Health Care as a "Sensitive Place".

Importance: Hospitals are hot zones of the US gun injury epidemic. To shelter these facilities from the dangers of gun violence, state legislatures have enacted laws to reduce the carrying of firearms on hospital premises. However, these efforts currently face serious Second Amendment challenges in federal courts. The ongoing legal battles, which have wide-ranging implications for patient and clinician safety as well as public health generally, are setting the stage for a Supreme Court case that may decide the fate of firearm regulations in US hospitals. A permissible pathway for advancing sensible gun regulation in hospitals is urgently needed. Observations: Since the Supreme Court established a new constitutional test for firearm laws in New York State Rifle & Pistol Association v Bruen (2022), states now face unprecedentedly high barriers to enacting health-protecting legislation regarding firearms. Post-Bruen, the Supreme Court requires that laws be consistent with "this Nation's historical tradition of firearms regulation." This means that states hoping to enact laws barring public carry of firearms in hospitals must demonstrate that hospitals are a "sensitive place" as a historical matter (ie, analogous to a location where firearms were traditionally restricted). By reasoning from analogy, it is clear several historical comparators exist for regulating firearms in hospitals. Although the hospital (as understood today) did not exist in the 1700s, it is sufficiently analogous to asylums and schools, to name a few examples. These settings all share a common denominator with the modern-day hospital: serving vulnerable populations or individuals who may be at heightened risk of misusing firearms. Conclusions and Relevance: The Supreme Court's interpretation of the Second Amendment right to bear arms is threatening democratically enacted laws seeking to shelter hospitals from firearm violence. However, it is clear that hospitals and other health care settings are a sensitive place with compelling historical analogies. Policymakers' strategic deployment of the sensitive places designation, along with its rightful judicial recognition in the hospital setting, are critical to upholding laws that protect health care facilities, patients, and professionals from firearm violence-a conclusion consistent with the US Constitution, history, medical ethics, and common sense.

Multiple bouts of blood feeding in mosquitoes allow prolonged survival and are predicted to increase viral transmission during drought.

Survival through periods of drought is critical for mosquitoes to reside in semi-arid regions with humans, but water sources may be limited. Previous studies have shown that dehydrated mosquitoes will increase blood feeding propensity, but how this would occur over extended dry periods is unknown. Following a bloodmeal, prolonged exposure to dry conditions increased secondary blood feeding in mosquitoes by nearly two-fold, and chronic blood feeding allowed mosquitoes to survive twenty days without access to water sources. This refeeding did not alter the number of eggs generated, suggesting this refeeding is for hydration and nutrient replenishment. Exposure to desiccating conditions following a bloodmeal resulted in increased activity, decreased sleep levels, and prompted a return of CO2 sensing before egg deposition. The increased blood feeding during the vitellogenic stage and higher survival during dry periods are predicted to increase pathogen transmission and explain the elevated levels of specific arbovirus cases during dry conditions. These results solidify our understanding of the role of dry periods on mosquito blood feeding and how mosquito dehydration contributes to vectorial capacity and disease transmission dynamics.

Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.

BACKGROUND: Large language models (LLMs) have the potential to support promising new applications in health informatics. However, practical data on sample size considerations for fine-tuning LLMs to perform specific tasks in biomedical and health policy contexts are lacking. OBJECTIVE: This study aims to evaluate sample size and sample selection techniques for fine-tuning LLMs to support improved named entity recognition (NER) for a custom data set of conflicts of interest disclosure statements. METHODS: A random sample of 200 disclosure statements was prepared for annotation. All "PERSON" and "ORG" entities were identified by each of the 2 raters, and once appropriate agreement was established, the annotators independently annotated an additional 290 disclosure statements. From the 490 annotated documents, 2500 stratified random samples in different size ranges were drawn. The 2500 training set subsamples were used to fine-tune a selection of language models across 2 model architectures (Bidirectional Encoder Representations from Transformers [BERT] and Generative Pre-trained Transformer [GPT]) for improved NER, and multiple regression was used to assess the relationship between sample size (sentences), entity density (entities per sentence [EPS]), and trained model performance (F1-score). Additionally, single-predictor threshold regression models were used to evaluate the possibility of diminishing marginal returns from increased sample size or entity density. RESULTS: Fine-tuned models ranged in topline NER performance from F1-score=0.79 to F1-score=0.96 across architectures. Two-predictor multiple linear regression models were statistically significant with multiple R2 ranging from 0.6057 to 0.7896 (all P<.001). EPS and the number of sentences were significant predictors of F1-scores in all cases ( P<.001), except for the GPT-2_large model, where EPS was not a significant predictor (P=.184). Model thresholds indicate points of diminishing marginal return from increased training data set sample size measured by the number of sentences, with point estimates ranging from 439 sentences for RoBERTa_large to 527 sentences for GPT-2_large. Likewise, the threshold regression models indicate a diminishing marginal return for EPS with point estimates between 1.36 and 1.38. CONCLUSIONS: Relatively modest sample sizes can be used to fine-tune LLMs for NER tasks applied to biomedical text, and training data entity density should representatively approximate entity density in production data. Training data quality and a model architecture's intended use (text generation vs text processing or classification) may be as, or more, important as training data volume and model parameter size.

Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.

Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in the past two decades, but most prior studies were characterized by several shortcomings (e.g., using homogenous, small samples), resulting in crucial knowledge gaps and a limited understanding concerning empirically based risk factors for PPU. This study aimed to identify the most robust risk factors for PPU using a preregistered study design. Independent laboratories' 74 preexisting self-report data sets (Nparticipants = 112,397; Ncountries = 16) were combined to identify which factors can best predict PPU using an artificial intelligence-based method (i.e., machine learning). We conducted random forest models on each data set to examine how different sociodemographic, psychological, and other characteristics predict PPU, and combined the results of all data sets using random-effects meta-analysis with meta-analytic moderators (e.g., community vs. treatment-seeking samples). Predictors explained 45.84% of the variance in PPU scores. Out of the 700+ potential predictors, 17 variables emerged as significant predictors across data sets, with the top five being (a) pornography use frequency, (b) emotional avoidance pornography use motivation, (c) stress reduction pornography use motivation, (d) moral incongruence toward pornography use, and (e) sexual shame. This study is the largest and most integrative data analytic effort in the field to date. Findings contribute to a better understanding of PPU's etiology and may provide deeper insights for developing more efficient, cost-effective, empirically based directions for future research as well as prevention and intervention programs targeting PPU. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using games to understand the mind.

Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behaviour in more complex, ecological settings than traditional laboratory experiments. By being fun, games allow researchers to study new questions in cognition such as the meaning of 'play' and intrinsic motivation, while also supporting more extensive and diverse data collection by attracting many more participants. We describe the advantages and drawbacks of using games relative to standard laboratory-based experiments and lay out a set of recommendations on how to gain the most from using games to study cognition. We hope this Perspective will lead to a wider use of games as experimental paradigms, elevating the ecological validity, scale and robustness of research on the mind.

Why Do People Watch Pornography? Cross-Cultural Validation of the Pornography Use Motivations Scale (PUMS) and Its Short Form (PUMS-8).

Motivations for pornography use may vary across gender identities, sexual orientations, and geographical regions, warranting examination to promote individual and public health. The aims of this study were to validate the Pornography Use Motivations Scale (PUMS) in a diverse, multicultural sample, and develop a short form (PUMS-8) that can assess a wide range of pornography use motivations. Using data from 42 countries (N = 75,117; Mage = 32.07; SDage = 12.37), enabled us to thoroughly evaluate the dimensionality, validity, and reliability of the Pornography Use Motivations Scale (PUMS), leading to the development of the more concise PUMS-8 short scale. Additionally, language-, nationality-, gender-, and sexual-orientation-based measurement invariance tests were conducted to test the comparability across groups. Both the PUMS and the PUMS-8 assess eight pornography use motivations, and both demonstrated excellent psychometric properties. Sexual Pleasure emerged as the most frequent motivation for pornography use across countries, genders, and sexual orientations, while differences were observed concerning other motivations (e.g. self-exploration was more prevalent among gender-diverse individuals than men or women). The motivational background of pornography use showed high similarity in the examined countries. Both the PUMS and the PUMS-8 are reliable and valid measurement tools to assess different types of motivations for pornography use across countries, genders, and sexual orientations. Both scales are recommended for use in research and clinical settings.

The cryoEM structure of the Hendra henipavirus nucleoprotein reveals insights into paramyxoviral nucleocapsid architectures.

We report the first cryoEM structure of the Hendra henipavirus nucleoprotein in complex with RNA, at 3.5 Å resolution, derived from single particle analysis of a double homotetradecameric RNA-bound N protein ring assembly exhibiting D14 symmetry. The structure of the HeV N protein adopts the common bi-lobed paramyxoviral N protein fold; the N-terminal and C-terminal globular domains are bisected by an RNA binding cleft containing six RNA nucleotides and are flanked by the N-terminal and C-terminal arms, respectively. In common with other paramyxoviral nucleocapsids, the lateral interface between adjacent Ni and Ni+1 protomers involves electrostatic and hydrophobic interactions mediated primarily through the N-terminal arm and globular domains with minor contribution from the C-terminal arm. However, the HeV N multimeric assembly uniquely identifies an additional protomer-protomer contact between the Ni+1 N-terminus and Ni-1 C-terminal arm linker. The model presented here broadens the understanding of RNA-bound paramyxoviral nucleocapsid architectures and provides a platform for further insight into the molecular biology of HeV, as well as the development of antiviral interventions.

A Case Report of Subacute Infective Endocarditis Presenting With Extreme Weight Loss, Aortic Regurgitation, and Splenic Infarct.

We present an insightful case of a middle-aged male who presented to the emergency department (ED) with complaints of excessive weight loss, accompanied by shortness of breath and vomiting. Consequently, this case explores many facets of the pathophysiology of infective endocarditis (IE), including but not limited to the most heavily implicated microorganisms, symptoms, predispositions, and disease outcomes. IE is a pathology of variable presentation with uniquely extensive diagnostic criteria, making it a fascinating topic of medical discussion.

Comparing First-Year Engineering Student Conceptions of Ethical Decision-Making to Performance on Standardized Assessments of Ethical Reasoning.

The Defining Issues Test 2 (DIT-2) and Engineering Ethical Reasoning Instrument (EERI) are designed to measure ethical reasoning of general (DIT-2) and engineering-student (EERI) populations. These tools-and the DIT-2 especially-have gained wide usage for assessing the ethical reasoning of undergraduate students. This paper reports on a research study in which the ethical reasoning of first-year undergraduate engineering students at multiple universities was assessed with both of these tools. In addition to these two instruments, students were also asked to create personal concept maps of the phrase "ethical decision-making." It was hypothesized that students whose instrument scores reflected more postconventional levels of moral development and more sophisticated ethical reasoning skills would likewise have richer, more detailed concept maps of ethical decision-making, reflecting their deeper levels of understanding of this topic and the complex of related concepts. In fact, there was no significant correlation between the instrument scores and concept map scoring, suggesting that the way first-year students conceptualize ethical decision making does not predict the way they behave when performing scenario-based ethical reasoning (perhaps more situated). This disparity indicates a need to more precisely quantify engineering ethical reasoning and decision making, if we wish to inform assessment outcomes using the results of such quantitative analyses.

Identifying trigger cues for hospital blood transfusions based on ensemble of machine learning methods.

BACKGROUND: Traumatic shock is the leading cause of preventable death with most patients dying within the first six hours from arriving to the hospital. This underscores the importance of prehospital interventions, and growing evidence suggests prehospital transfusion improves survival. Optimizing transfusion triggers in the prehospital setting is key to improving outcomes for patients in hemorrhagic shock. Our objective was to identify factors associated with early in-hospital transfusion requirements available to prehospital clinicians in the field to develop a simple algorithm for prehospital transfusion, particularly for patients with occult shock. METHODS: We included trauma patients transported by a single critical care transport service to a level I trauma center between 2012 and 2019. We used logistic regression, Fast and Frugal Trees (FFTs), and Bayesian analysis to identify factors associated with early in-hospital blood transfusion as a potential trigger for prehospital transfusion. RESULTS: We included 2,157 patients transported from the scene or emergency department (ED) of whom 207 (9.60%) required blood transfusion within four hours of admission. The mean age was 47 (IQR = 28 - 62) and 1,480 (68.6%) patients were male. From 13 clinically relevant factors for early hospital transfusions, four were incorporated into the FFT in following order: 1) SBP, 2) prehospital lactate concentration, 3) Shock Index, 4) AIS of chest (sensitivity = 0.81, specificity = 0.71). The chosen thresholds were similar to conventional ones. Using conventional thresholds resulted in lower model sensitivity. Consistently, prehospital lactate was among most decisive factors of hospital transfusions identified by Bayesian analysis (OR = 2.31; 95% CI 1.55 - 3.37). CONCLUSIONS: Using an ensemble of frequentist statistics, Bayesian analysis and machine learning, we developed a simple, clinically relevant prehospital algorithm to help identify patients requiring transfusion within 4 h of hospital arrival.