PLOS Digital Health

Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficulty. Methods: We assembled 125 multiple-choice questions on dental antibiotic prescribing from eight peer-reviewed studies (2017-2023). GPT-4o answered each question and generated a clinical justification. Accuracy was assessed against source-study answer keys and examined across difficulty quartiles. Justifications were evaluated using an adapted 12-axis human-evaluation framework assessing scientific consensus, extent and likelihood of harm, inappropriate and missing content, bias, and both correct and incorrect comprehension, retrieval, and reasoning. Prophylaxis-specific questions were analysed separately. Results: GPT-4o correctly answered 72% of questions. Accuracy remained relatively stable across difficulty quartiles (78%, 78%, 65%, 70%). Experts rated 95.4% of justifications positively across the 12 axes. Comprehension, retrieval, and reasoning each exceeded 96.2% positive ratings. Missing content was the main weakness (7.8%), and 7.1% of justifications showed a moderate-to-severe potential for harm. Performance on prophylaxis-specific questions (98.1%) exceeded non-prophylaxis questions (93.0%). Conclusions: GPT-4o demonstrated moderate-to-high proficiency and clinically defensible reasoning in dental antibiotic prescribing questions. However, residual risks indicate that it is not suitable for unsupervised clinical use but shows potential as a supervised AMS educational tool.

Large language models perform well on medical examinations, but users routinely challenge their answers and invoke professional roles, and it is unclear what a system does when a medical credential and a stated task-specific accuracy point in opposite directions. In a factorial experiment on 480 items from four Polish specialty examination sets and three consumer large language model systems (ChatGPT, Claude, Gemini), each item and system received eleven independent conversations. Conditions crossed attributed source role (medical student, experienced specialist), stated prior accuracy on similar questions (2/10, 8/10) and suggestion correctness. The primary outcome was adoption of a prespecified incorrect option when the baseline answer matched the official key, comparing a specialist described as 2/10 with a student described as 8/10. Baseline agreement with the key was 87.2% across 15,683 analyzable conversations. The incorrect option was adopted more often from the specialist described as 2/10 than from the student described as 8/10 (10.2% vs. 7.6%; adjusted risk difference +2.82 percentage points, 95% CI +0.65 to +4.99). Estimates varied across the three systems and only one system-specific interval excluded zero. In a prespecified exploratory analysis with a shared eligibility rule, correct suggestions were adopted far more often than incorrect ones (risk difference +35.7 percentage points, 95% CI +30.8 to +40.7), indicating selective rather than indiscriminate compliance. An incorrect suggestion from a specialist with low stated accuracy was therefore slightly more influential than the same suggestion from a student with high stated accuracy, although the difference was modest and varied across systems. Agreement reached only after a user has disclosed a preferred answer should not automatically be treated as an independent second opinion, and medical large language model systems should be evaluated on how they revise answers after such disclosure, not solely on initial accuracy.

Background Computerised cognitive testing may address the global challenge in identifying cognitive changes in people living with HIV scalably and affordably. We assessed a computerised battery (CB) of cognitive tests, in a prospective cohort (CONNECT) of people with HIV in a low-income peri-urban area of Cape Town, South Africa during a national programmatic switch from efavirenz- to dolutegravir-based antiretroviral therapy (ART). Methods We recruited 170 people with HIV and 91 people without HIV (controls) (140[82%] and 41[45%] followed up). The CB and gold-standard pen&paper cognitive testing (P&P) were performed at both timepoints. Technology familiarity/use questionnaire data were also collected. We compared performance in detecting lower group-level cognitive performance associated with efavirenz treatment. Furthermore, the CB was compared to P&P in classifying individuals with low cognitive performance, correlation of global test scores and domain-level scores between batteries, and practice effects between timepoints. Exploratory principal component analysis was also performed. Results People with HIV on efavirenz at baseline had lower performance on the computerised battery than controls, {Delta}T=2.6, p=0.0047. This difference was lost after switching to dolutegravir-based ART at follow-up. CB and P&P global T were moderately correlated (R2=0.203, p<0.001), and the CB performed moderately in classification of low cognitive performance against the gold standard (AUC 0.70, sensitivity 0.52, specificity 0.76, PPV 0.40, and NPV 0.84). Selecting the first three principal components improved both classification of low cognitive performance (AUC 0.77) and correlation strength with P&P global T (R2=0.3, p<0.001). The CB did not show practice effects. Most participants owned a mobile phone (95%, 85.9% of these smartphones). Performance was better in smartphone owners ({Delta}T=1.8) and computer owners (23%, {Delta}T=1.8). Conclusions Delivering computerised cognitive testing was feasible in this low-income southern African setting. The CB showed reasonable construct validity (detecting known lower cognitive performance associated with efavirenz-ART) and may detect broad cognitive characteristics such as processing speed and accuracy. However, correlation of CB results with gold standard P&P testing was low-moderate and may limit its applicability as a diagnostic tool. This might be improved by including a wider range of cognitive domains tested in the CB, or data driven analysis. Brief CBs may fulfil an initial screening role, followed by more detailed clinical assessment.

Background: Consumer use of AI chatbots for health advice is rising, yet triage safety relative to established services remains unclear. Australia's Healthdirect, a government-backed symptom checker with 2.4 million uses in FY2024-25, remains unevaluated against frontier large language models (LLMs), and whether premium subscriptions improve triage safety remains unexplored. This study compared the triage accuracy and safety of Healthdirect against six LLM configurations across ChatGPT, Claude, and Gemini, assessed whether paid subscriptions improve triage safety, and characterised each system's error patterns. Methods: Forty-five clinical vignettes from the Semigran et al. benchmark spanning emergency, non-emergent, and self-care categories (15 each) were evaluated across seven systems. Healthdirect was tested following a seven-rule interaction protocol. LLMs were evaluated using first-person patient-language prompts under free-tier and paid-tier conditions. Outcomes were triage accuracy, emergency sensitivity, under-triage, and critical misses, analysed using Cochran's Q, Bonferroni-corrected McNemar tests, Cohen's kappa, and Wilson intervals. Findings: Triage accuracy differed significantly (Cochran's Q = 36.79, p < 0.001). Healthdirect achieved 48.9% accuracy (95% CI 35.0% to 63.0%; kappa = 0.233) versus 73.3% to 86.7% for LLMs (kappa = 0.600 to 0.800). Healthdirect operated under conservative interactive defaults while LLMs received complete information in a single prompt, which may have disadvantaged Healthdirect. Emergency sensitivity was 46.7% versus 80.0% to 86.7% for LLMs. Healthdirect produced two critical misses; no LLM produced any across 270 evaluations (95% CI 0% to 1.4%). When LLMs undertriaged, they recommended GP care rather than self-care. No tier differences were significant (all p > 0.05), and most systems over-triaged self-care cases. Interpretation: Frontier LLMs demonstrated higher triage accuracy and safer error profiles than Healthdirect. All LLMs avoided critical misses; Healthdirect did not. Premium subscriptions did not significantly improve triage safety. These findings support clinical governance decisions about whether LLMs warrant formal evaluation alongside government-backed symptom checkers.

The openEHR standard provides an open, vendor-neutral architecture for clinical data repositories (CDRs), yet its real-world deployment has not been systematically documented. We conducted a dual-perspective survey combining a vendor survey of openEHR CDR providers with a community survey of openEHR practitioners. Eleven vendor organisations reported deployments across 22 countries and over 100 institutions and health regions. A complementary community survey (n=29, 17 countries) provided context on regulatory environments, adoption drivers, and barriers. Combined, the surveys cover 28 countries, 26 of them with a reported openEHR CDR deployment. Three findings emerge: openEHR has achieved national-scale presence through two distinct channels. Through vendor-market convergence, openEHR-based systems cover the majority of regional health authorities without a national mandate, including 19 of 21 Swedish regions, 3 of 4 Norwegian health regions, and 16 of 21 Finnish wellbeing services counties. Through national health record adoption, governments have built or procured national systems on openEHR as their technical foundation, including Ireland, Malta, Greece, Jamaica and Slovenia. Across Europe, this constitutes an openEHR-based interoperability infrastructure already in place across multiple EU member states. We identified no country in which openEHR is named in binding national regulation, creating structural fragility and an unrealised opportunity for alignment with the European Health Data Space (EHDS). Second, 61% of deployments serve primary use only, and 12% support both primary and secondary use. Third, lack of openEHR-specific knowledge is the most consistent adoption barrier across all geographies and deployment tiers. Adoption is driven by practitioner need and innovation, not by regulatory mandate.

Disseminating study results to participants is an ethical responsibility for researchers but remains uncommon in low- and middle-income countries, and participants preferences for receiving study results are poorly understood. This study examined study result dissemination preferences among pregnant women in a phase III malaria prevention trial in Papua New Guinea (PNG). Participants completed an interviewer-administered questionnaire (survey) assessing their interest in and motivation for receiving trial results and preferences for dissemination methods and content. Associations between participants characteristics and dissemination preferences were explored using multivariable logistic regression analysis. Of 1172 trial participants, 96.0% (1125/1172) completed the survey, and of these 99.6% (1121/1125) wanted to learn about the trial results. The main motivation factors driving participants interest were an acknowledgment of their contribution to research (51.7%; n=579) and a better understanding of the study (45.0%; n=505). Most participants (78.9%; n=884) wanted to learn about the trial findings through written summary and a group meeting with other participants at the nearest clinic (31.1%, n=349). Multivariable regression analysis indicated that participants from rural/peri-urban clinics were more likely to choose non-electronic media dissemination approaches such as a group meeting as compared to urban-dwelling participants. Frequently selected items (>50% of participants) for content included information regarding good results of the study, purpose of the study, medical treatment advances, results specific to me, and how study was conducted. There was heterogenicity in the preference for dissemination content: compared to urban clinics rural clinics are less likely to want to learn about how and why study was conducted and medical and scientific advances. Overall, the majority wanted to learn about trial results, highlighting the importance of integrating dissemination into research activities in PNG. Variation in preferences for mode and content of dissemination between study clinics suggests that dissemination activities could be tailored to local context and preferences.

In this work, we demonstrate the unprecedented value of NIH's "All of Us Research Program" (AoURP) dataset in studying maternal morbidity and building predictive machine learning (ML) models across heterogeneous populations in the United States. We developed robust and data-driven preprocessing pipelines to curate a longitudinal, multi-site, multimodal, and demographically diverse pregnancy dataset (20,253 subjects; 27,525 pregnancy episodes) from AoURP data, using electronic health records (EHR) (Conditions, Labs, Measurements) and survey responses (Social Determinant of Health (SDoH)), focusing on 7 crucial maternal health adverse outcomes. After characterizing data quality, missingness, and heterogeneity, we performed statistical correlation analysis to identify risk factors. We subsequently developed XGBoost and sequential LSTM models to predict the adverse outcomes, reaching state-of-the-art performance for multiple outcomes. We conducted model interpretability post-hoc analysis to understand success points and fairness analysis to evaluate implications for socio-economic disparities. Four practicing physicians reviewed the set of statistically significant and ML model identified features to assess their clinical validity and novelty. Most features identified through either statistical correlations or ML feature importance analysis aligned with known clinical risk factors. Several features were identified that the ML models used but that are not currently used in clinical practice and may merit further clinical investigation. Fairness analysis revealed certain associations with SDoH and age highlight areas that warrant continued monitoring. Overall, we demonstrate that meaningful populational level patterns can be extracted, and high-performing machine learning models can be trained on this longitudinal, diverse, multi-site dataset. Important risk features, particularly novel ones identified, if validated, could inform new strategies for maternal care or enable development and validation of outcome-specific, clinically deployable ML models.

Background. Routine service databases are attractive sources of training labels for clinical prediction models, but the processes that write those labels are rarely audited before the labels are used. In a deployed community cognitive-screening programme, we audited the routine cognitive-status label, built a matrix of twenty-four model arms over the same patients under a specialist reference standard, and measured what each supervision choice bought or cost. Methods. The study cohort is the 672 individuals whose cognitive status was recorded by a titled (attending-or-above) physician, that record being the reference standard; after holding out one institution entirely, a development panel of 642 individuals at 38 institutions. The routine cognitive-status label these individuals also carry was first audited at the operator level: for each data-entry account we counted diagnoses entered and the proportion recording any impairment, and tested a competing bulk-timestamp explanation. Twenty-four arms span the supervision choices such a programme faces: an incumbent 21-variable logistic regression; local language models (Qwen2.5-1.5B/3B, Qwen3-4B/8B) zero-shot, with chain-of-thought, fine-tuned on physician labels, on routine labels with and without decontamination, or on a proxy scale-band task; preference-optimised (DPO) and reinforcement-trained (GRPO) variants; a proprietary frontier model queried zero-shot; and knowledge distillation of that frontier model into the regression and into the local 4B, using 943 teacher-labelled records from the programme's unlabelled pool. All arms are scored out-of-fold under one five-fold split grouped on registry-resolved institution clusters (no cluster spans a fold); paired contrasts use a 2,000-draw cluster bootstrap. Results. 181 operator accounts (each entering at least 100 diagnoses with zero recorded impairments) account for 45,315 rows - 40.5% of the outcome column; recorded impairment falls monotonically with account volume (15.7% for 1-9 rows to 0.7% for 500-999); a bulk-timestamp explanation was tested and refuted, identifying the write-time column as a migration artefact. Under the specialist standard, no locally fine-tuned arm beat the incumbent regression (AUROC 0.926): physician-label SFT reached 0.924 (4B), DPO 0.881, and GRPO 0.789; the pre-registered two-stage proxy-then-RL recipe was worse than its single-stage contaminated baseline (-0.030, 95% CI -0.077 to -0.004). Chain-of-thought reduced discrimination at every size (-0.072, -0.080, -0.041 at 1.5B/3B/4B; -0.012, n.s., at 8B). The frontier model scored 0.932 (vs. regression +0.007, n.s.). The distilled 4B reached 0.940 - above the incumbent (+0.014, 0.004 to 0.031) and above its own teacher (+0.008, 0.001 to 0.017) - with near-teacher calibration; it reached the teacher's level by 50 teacher labels and changed little beyond 200. Conclusions. The audit and the arm matrix support one deployment recipe: audit the routine label at the operator level before training on it; do not expect fine-tuning, preference optimisation, or reinforcement learning on a few hundred specialist cases to beat a well-calibrated regression; and if a frontier model is available but undeployable, spend a bounded number of queries on it as a labelling instrument and distil. A companion paper uses these frozen predictions to quantify how evaluation design choices compare with model choice.

Agentic workflows can coordinate modelling, but balancing predictive performance, measurement burden and reproducibility is unclear. We developed DXA Agent, an agentic workflow for dual-energy X-ray absorptiometry (DXA) outcomes integrating planning, feature-model refinement, tools, provenance and hypothesis-generating interpretation. Models were independently developed and tested in UK Biobank (5,318 participants) and the National Health and Nutrition Examination Survey (NHANES; 3,777 participants), using cost-efficient and no-limit strategies. Across 20 UK Biobank and three NHANES bone mineral density sites, cost-efficient models achieved lower RMSE and higher R2 than the best conventional comparator, with median relative RMSE reductions of 10.9% and 9.9%, respectively. Classification was task dependent: UK Biobank osteoporosis averaged AUROC 0.839 and PR-AUC 0.182, whereas NHANES performance was comparable with conventional models. Higher-burden features did not consistently improve prediction. These retrospective, cohort-internal findings position DXA Agent as an inspectable, measurement-burden-aware research workflow requiring independent prospective validation.

BACKGROUND Generative AI (genAI) chart summarization tools embedded in electronic health records (EHRs) are being rapidly deployed across U.S. health systems. Although these tools represent a promising solution to alleviate cognitive burdens, their effects have not been examined in randomized-clinical trials (RCTs). METHODS In this pragmatic RCT at a single academic health system, 284 outpatient clinicians across forty-two specialties were assigned 1:1 to Epic's outpatient chart summarization tool or a usual-care control arm over 90 days, from February 23 to May 23, 2026. The primary outcome was physician task load (PTL) adapted for pre-charting. Prespecified exploratory outcomes included additional validated psychometrics as well as usability, safety, and time-based measures. Descriptive statistics included interaction and usage of the tool. RESULTS Of 74,474 AI chart summaries generated, 14.2% were interacted with by a clinician; the proportion of generated summaries interacted with declined from 21.5% in month 1 to 10.5% in month 3, and the proportion of clinicians using the tool at least once per month declined from 88.7% to 66.2%. The adjusted between-arm difference in PTL at follow-up favored the intervention arm (scale 0-400; -27.4; 95% CI, -49.4 to -5.3; P=0.02). Among the Professional Fulfillment Index (PFI; scale 0-4, lower=better) psychometrics, overall burnout (-0.20; 95% CI, -0.38 to -0.01) and work exhaustion (-0.24; 95% CI, -0.47 to -0.02) were lower in the intervention arm, with little difference in overall professional fulfillment (+0.04; 95% CI, -0.16 to 0.25). Charting time per encounter showed no significant between-arm difference during steady state (-1.2 seconds; 95% CI, -19.0 to 16.6). The net promoter score was -22, indicating that on average, clinicians did not recommend the tool. Among free-text respondents, 57.1% reported at least one concern, most commonly tool limitations or inaccurate information. No adverse patient safety events or near-misses were reported. CONCLUSION An EHR-integrated AI chart summarization tool modestly reduced physician task load and was associated with lower burnout, without time savings and against declining engagement. Sustained usage and oversight of reported inaccuracies remain open challenges.

The widespread adoption of clinical large language models (LLMs) introduces significant risks of automation bias, premature closure, and clinician deskilling. Current interpretability paradigms, including latent space trajectories, Concept Activation Vectors, and Concept Bottleneck Models, suffer from topological stagnation, metric distortion, and epistemic occlusion, frequently masking intermediate diagnostic uncertainty behind falsely confident outputs. To address these structural vulnerabilities, this paper introduces a novel closed-loop, multi-agent framework designed to quantify and visualize dynamic epistemic uncertainty in clinical LLM reasoning. By coupling predictive Shannon entropy with non-linear Isometric Feature Mapping (ISOMAP), the architecture projects high-dimensional inference state vectors onto a calibrated two-dimensional latent space, thereby assigning a quantifiable thermodynamic energy state to the reasoning path to track diagnostic velocity, cognitive momentum, and trajectory efficiency across sequential diagnostic rounds. Pilot validation across representative emergency medicine scenarios demonstrated distinct topological and information-theoretic behaviors: unconfounded cases (cerebellar infarction) exhibited smooth geodesic progression toward the ground truth alongside monotonic Shannon entropy decay from 2.15 to 1.74; noisy environments with ambiguous findings (spontaneous pneumothorax) suffered from trajectory wandering, local minimum traps, and high sustained entropy (~2.41) due to insufficient repulsive weighting for negative evidence; and triage-conflicted cases (acute cholangitis) achieved precise geometric proximity to the true node but experienced top-1 rank stagnation because the model conflated acute severity triage (sepsis) with anatomical etiology. By rendering machine hesitation and cognitive divergence visually auditable before final diagnostic crystallization, this geometric-information framework enables dynamic trust calibration and human-AI co-regulation at the point of care while establishing a clear mathematical foundation for future architectural interventions, such as dual-channel safety decoupling and non-linear repulsive weighting. Moving forward, validating these architectural enhancements across large-scale electronic health record databases and prospective clinical trials will be essential to realize its full clinical utility, establishing a foundational blueprint for safe, transparent, and cognitively synergistic AI integration in future medical practice. By rendering the LLM's reasoning process visually auditable, this framework lays the groundwork for capturing and externalizing the clinician's own cognitive patterns within the AI, forming a coupled system. This enables the explicit visualization of cognitive gaps between physician hypotheses and AI inferences, transforming the interaction from simple answer-checking into a dynamic learning process for both human and machine that prevents diagnostic oversight. Ultimately, because the responsibility for final clinical decision-making remains with the human practitioner, this framework serves as a vital decision-support mechanism. Moving forward, validating these architectural enhancements across large-scale electronic health record databases and prospective clinical trials will be essential to realize its full clinical utility, establishing a foundational blueprint for safe, transparent, and cognitively synergistic AI integration in future medical practice.

Background: Unstructured biomedical data, such as echocardiography reports, are rich in information but time consuming to analyze at scale. Rule-based, regular expression-driven terminology mapping can only extract individual variables while large language models (LLMs) offer scalable and clinically meaningful interpretations of heterogeneous disease processes. Right ventricular dysfunction (RVD) is an example of a multifactorial disease state in which key structural and physiologic features are captured both narratively and in structured fields, making it an ideal test case for evaluating whether LLMs can recover complex phenotypes that rules based methods routinely miss. Purpose: To compare an LLM-based extraction method to a conventional rules-based schema for identifying and phenotyping echocardiographic features associated with RVD in a large TTE dataset. Methods: MIMIC-III NOTE2NUM echocardiography reports (n = 45,794) were analyzed using GPT-4o-based LLM extraction deployed within a secure health system enclave and were benchmarked against echocardiographic measurements defined in the MIMIC-III dictionary schema. In MIMIC-III, PH was recorded qualitatively (mild/moderate/severe) based on tricuspid regurgitant (TR) jet velocity and then re-coded as present vs. absent. LLM based extraction defined RVD as (1) RV structural abnormality (>= 1 of hypertrophy, dilation, or wall hypo-/akinesis) or (2) RV pressure/volume overload (>= 2 of the following: estimated right atrial pressure > 8 mmHg, TR jet velocity > 2.8 m/s, fractional area change < 35%, tricuspid annular planar systolic excursion < 17 mm, S' < 9.5 cm/s, or E/e' > 14), with PH defined as estimated pulmonary artery systolic pressure > 35 mmHg or qualitative documentation of PH. Results: LLM extraction identified PH in 15,394 (33.6%), RV pressure/volume overload in 14,449 (31.6%), and RV structural abnormalities in 11,955 (26.1%). Co-occurrence was common: overload + structural changes in 9,380 (20.5%), overload + PH in 9,756 (21.3%), structural changes + PH in 6,183 (13.5%), and all three in 5,620 (12.3%). Using the MIMIC-III dictionary schema, PH prevalence was similar (15,371; 33.6%), but RV overload fields were captured less often (pressure overload 1,357 [3.0%], volume overload 1,128 [2.5%], pressure + volume overload 1,093 [2.4%]; any overload field 3,578 [7.8%]), and RV pressure/volume overload with PH was identified in only 731 (1.6%). Conclusions: LLM-based extraction outperforms rules-based schemas for identifying complex disease states not defined by any single variable. By synthesizing multifactorial signals, LLMs can phenotype RVD with higher fidelity and support population-level assessment. Further validation using multimodality imaging, invasive hemodynamics, and clinical outcome data is needed.

Acute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. This is especially important because a timely window for intervention is required as delayed detection leads to progressively worse outcomes. Existing machine learning and deep learning models have contributed to closing this gap, but their complexity, requiring hundreds to thousands of features, and lack of generalisation pose a limitation that prevents them from being integrated into clinical workflows across different electronic health-record ecosystems. This study presents a 37-feature XGBoost model trained on the MIMIC-IV dataset with 5.4% positive cases, with hyperparameters optimised via Optuna and probabilities calibrated using isotonic regression, designed for transportability across clinical settings. Validation was conducted internally using a temporal patient-level split simulating prospective deployment, training on 2008-2016 data and testing on 2017-2022 data"External validation was performed on the eICU Collaborative Research Database, a multi-centre dataset spanning 208 US hospitals, using the trained model without retraining. SHAP TreeExplainer was used to provide feature-level explainability for individual predictions. Internal testing yielded an AUROC score of 0.794 for predicting AKI onset within a 12-24 hour window. External validation produced a 0.750 AUROC without retraining. Equitable discrimination was observed across gender, age, chronic kidney disease presence, race, and AKI stages on both datasets, with a 95% internal CI of 0.789-0.799 confirming the model's estimate stability. These results suggest that clinically useful prediction systems are achievable with substantially fewer features than current models require.

Background: Newly available arm angle data offers a new dimension to understand rising rates of arm injury in MLB pitchers. Purpose: To evaluate the relationship between arm angle, pitch characteristics, and elbow and forearm injury in MLB pitchers.<br><br> Study Design: Retrospective cohort study; Level of evidence, 3 Methods: Statcast data from 2020 to 2025 and MLB injured list (IL) data were used to evaluate arm angle and pitch characteristics in relation to elbow and forearm injuries. Results are presented with and without requirements on prior season workload and for same-season and next-season injury incidence. A generalized additive model (GAM) was used to capture non-linear dependence and interactions between selected features and injury incidence to the elbow or forearm. Average marginal effect (AME) odds ratios are reported for main effect terms. Results: N = 3,812 pitcher-seasons were included. 29% pitchers who underwent UCLR did so in the same season as a forearm injury (tmean=44, tmedian=27 days to surgery). Arm angle, fastball usage, and their interaction were the three most predictive features. Arm angle was positively related to incidence of injury (ORmeanAME=1.014), fastball usage was inversely related to incidence of injury (ORmeanAME=0.243), and arm angle moderated the effect fastball usage at high arm angle, where increased usage was no longer protective. Slider velocity (ORmeanAME=1.072), spin rate (ORmeanAME=1.001), and usage (ORmeanAME=2.039) also significantly predicted injury risk. Fastball velocity was not significant in any fit, with ORmeanAME=0.999 across all fits. Fit-level Nagelkerke R2 values ranged from .019 to .052. Conclusion: Fastball usage and arm angle, not velocity, predicted elbow and forearm injury risk among MLB pitchers, and arm angle was the single most predictive feature. The heterogeneity of risk factors as a function of arm angle, and the novelty of MLB arm angle data, may explain why fastball usage has been previously underexplored as a risk factor. Keywords: baseball; arm angle; fastball velocity; fastball usage; spin rate; UCL; ulnar collateral ligament; elbow injury; forearm injury; Statcast

Routine childhood growth surveillance is a cornerstone of pediatric care. Growth pattern abnormalities are often early manifestations of chronic disease. Yet subtle abnormalities are frequently underrecognized, leading to diagnostic delays and avoidable morbidity. We introduce SPROUT (System for Pediatric Recognition Of Undiagnosed Trajectories), a generalized, multi-agent large language model (LLM) reasoning system designed to identify a broad spectrum of pediatric growth-related conditions from longitudinal electronic health records (EHRs) earlier than standard clinical practice. Using a large pediatric primary care EHR dataset, we developed and validated SPROUT as a two-stage system. First, a highly specific LLM screener flags concerning longitudinal growth patterns. Second, an Orchestrator module coordinates a multidisciplinary panel of LLM agents to generate a ranked differential diagnosis. To correct systemic reasoning errors, a Trainer module injects meta-knowledge into the panel via a dedicated "Learner" agent. Diagnostic capability was evaluated using a walk-forward, visit-by-visit simulation leading up to the diagnosis date. The SPROUT screener model achieved 98% (83/85) specificity and 28% (9/32) sensitivity on a gold-standard dataset of pediatric primary care patients when evaluated one year before the index date, and 100% specificity and 47% sensitivity when evaluated using longitudinal data up to the day of diagnosis. When applied to 300 control patients (i.e., healthy or undiagnosed), the screener flagged 15. Subsequent expert panel review confirmed high suspicion for undiagnosed pathology in 33% (5/15) of these cases. In chronological walk-forward validation on disease cases, the diagnostic engine identified conditions well before standard-of-care documentation. One year prior to clinical diagnosis, the system achieved sensitivities of 81% for type 1 diabetes mellitus, 56% for pituitary disorders, and 44% for celiac disease. The SPROUT multi-agent system demonstrates the ability to detect a significant portion of latent growth-related pediatric conditions months to years before current clinical standards while minimizing false positives. These results support its potential as a decision support tool for reducing diagnostic delays in pediatric care.

Background: Lumbar disc herniation, with or without concomitant disc degeneration, is a major cause of lumbar radiculopathy and low back pain, which also a key public musculoskeletal disorder without an exact pathophysiology. Studies have suggested that inflammatory cells and biochemical markers of inflammation also play an important role in lumbar radiculopathy in addition to nerve compression. The aim of the present study was to assess the association of selected circulatory inflammatory markers (CRP, hs-CRP and E-selectin) in patients with lumbar disc herniation without radiological degeneration (LDH) and lumbar disc herniation with radiological degeneration (LDHD). Materials & methods: This case-control study included 208 participants, comprising 104 patients with lumbar disc pathology and 104 controls. Patients were further stratified into LDH (n=67) and LDHD (n=37). Serum CRP, hs-CRP and E-selectin concentrations were measured. Results: Among the patients, 35.6 % presented with LDHD while 64.4 % had only LDH. Significantly increased median hs-CRP (p<0.001) and CRP (p<0.001) were observed in patients groups compared to controls, while CRP showing a consistent independent association across the combined disease (OR=1.68, 95% CI=1.33-2.14, p<0.001), LDHD (OR=1.62, 95% CI=1.16-2.20, p=0.005) and LDH (OR=1.69, 95% CI=1.30-2.20, p<0.001) multivariable models. No significant difference was observed in serum E-selectin between the study groups. Multivariable models incorporating inflammatory and clinical variables demonstrated substantially greater discriminatory performance than individual biomarkers alone. Conclusion: Elevated circulating CRP and hs-CRP concentrations were associated with lumbar disc pathology, with CRP showing a consistent independent association across the combined disease, LDH and LDHD multivariable models, whereas E-selectin showed no significant association. Multivariable models incorporating inflammatory and clinical variables demonstrated greater discriminatory performance than individual biomarkers. These findings support a potential systemic inflammatory component in lumbar disc pathology, although the cross-sectional nature of the measurements does not establish causality or a local inflammatory response within the disc.

Introduction Safe and timely blood availability remains a major global health challenge, especially in low- and middle-income countries. Digital tools may accelerate donor contact, but digital reachability alone does not ensure that people will notice, trust and act on urgent requests to support blood donation efforts. We examined factors associated with anticipated engagement in digitally coordinated urgent blood-donor mobilisation among digitally reachable adults in Ghana. Methods We conducted a cross-sectional online survey from September 2025 to January 2026 across Ghana's 16 regions. Participants were recruited via Facebook advertising and snowball sampling. Factors associated with urgent blood-donor mobilisability were assessed under four criteria: high future-donation willingness; high willingness to install a trusted donation app; high willingness to respond to a trusted urgent-request; and high practical flexibility to leave current activities. Descriptive analyses and multivariable logistic regression examined prevalence and associated factors. Results Among 1,067 participants, 577 (54.1%) met all four criteria. Future-donation willingness (91.8%), trusted-app installation willingness (83.2%) and trusted-request response willingness (82.7%) were common, whereas practical flexibility was lower (66.6%). In the adjusted model, high formal health-system trust (adjusted OR (AOR) 3.95, 95% CI 2.08-7.50), high digital-response readiness (AOR 2.26, 1.66-3.08), previous donation (AOR 1.47, 1.08-2.01), high donation knowledge (AOR 1.42, 1.03-1.97) and willingness to donate to strangers were positively associated with high mobilisability. Women (AOR 0.60, 0.43-0.83), participants reporting a work-schedule barrier (AOR 0.43, 0.29-0.66) and those travelling over 30 min to the nearest healthcare facility at night (AOR 0.66, 0.45-0.96) had lower adjusted odds. Conclusions Digital reachability and stated donation willingness may overestimate the population pool available for emergency donation. Digital blood-donor solutions should consider verifiable health-system requests, account for response readiness and current availability, and connect willing individuals with accessible collection options and transport support where needed.

Introduction: Risk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch. Methods: We used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation-Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles. Results: Bup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries. Conclusion: This development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.

Background There has been a proliferation of new person-centered and human rights-based contraception measures in recent years, though their application in research remains limited. Improved measures offer an opportunity to examine how contraceptive decision-making agency relates to individuals ability to act in line with their contraceptive preferences. We sought to assess the association between contraceptive agency and subsequent Preference-aligned Fertility Management (PFM) over 12 months in a cohort of women in rural Uganda. Methods We analyzed data from a prospective cohort study conducted in five largely rural Ugandan districts from 2022 to 2024. Data were collected at baseline, 6 and 12 months from a convenience sample of women who were new users of contraception or not using contraception. We used mixed-effects logistic regression models to examine the association between baseline Agency in Contraceptive Decisions Scale overall and subscale scores and future PFM Index scores at 6 and 12 months, assessing whether associations varied over time using interaction terms for follow-up time point. We used interactions between agency scores and follow-up visit to assess whether associations differed between the 6- and 12-month visits. We assessed effect modification by age group and baseline contraceptive method category using three-way interaction terms and predicted probabilities. Results The analytic sample comprised 2,227 women. The percentage of women practicing PFM increased from 85.7% at baseline to 93.3% at 12 months. A one-unit increase in Agency in Contraceptive Decisions Scale score was associated with higher odds of subsequent PFM (aOR: 1.68, 95% CI: 1.10-2.54). Subscales 3 (knowledge aligned with preferences) and 4 (control over use or non-use) of the Agency in Contraceptive Decisions Scale were significantly associated with future PFM (aOR: 1.31, 95% CI: 1.04-1.66 and aOR: 1.27, 95% CI: 1.06-1.51, respectively). The association between overall contraceptive agency and PFM did not differ between the 6- and 12-month visits. Three-way interaction tests suggested that the associations between the overall Agency in Contraceptive Decisions Scale score and the PFM outcomes varied jointly by age group and baseline contraceptive method category: overall PFM Index (p<0.001), PFM1 (p=0.011), and PFM2 (p<0.001). Conclusion Our findings suggest that higher levels of contraceptive agency may help women act in line with their contraceptive preferences. Increasing womens knowledge and control over contraceptive use may be particularly essential for preferred contraceptive use. The findings also suggest that the association between contraceptive agency and PFM may vary by womens age group and the method of choice, though further exploration is necessary to examine this influence.

Purpose: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality, yet screening participation remains low. We evaluated whether a brief informational video nudge delivered immediately before a scheduled clinical encounter increased LCS ordering and baseline LCS completion. Patients and Methods: We conducted a randomized feasibility trial within Kaiser Permanente Colorado from March through October 2025. LCS-eligible patients with an upcoming primary care or pulmonology appointment were assigned to intervention or usual care based on birth month. Intervention patients were split into two group, a group who received the LCS informational video nudge via text message within 24 hours of an eligible appointment; and second group who received the text plus a QR code video link during appointment rooming. Outcomes included LCS orders, baseline LCS-LDCT completion, and video engagement. Multivariable logistic regression was used to evaluate factors associated with LCS ordering. Results: Among 1,093 patients, 549 were assigned to intervention and 544 to usual care. Intervention patients were more likely to receive an LCS order within 1 day of their appointment (22.6% vs 16.4%; p=.010) and any time during follow-up (32.6% vs 24.1%; p=.002). Baseline LCS-LDCT completion was 51% higher in the intervention group, although the difference was not statistically significant (8.6% vs 5.7%; p=.078). Among the intervention group, 93 individuals (17%) viewed the video, generating 114 total views, and viewers watched an average of 79% of the video. Most views (82.5%) occurred through text-message delivery rather than QR codes. Conclusion: A brief, low-burden LCS informational video delivered immediately before a clinical encounter and integrated into existing workflows significantly increased LCS ordering and was associated with higher screening completion. Timely, scalable digital nudges may provide an effective strategy for improving LCS participation. Based on the observed effectiveness, feasibility, and efficiency of the intervention, KPCO incorporated the behavioral nudge into standard clinical care in February 2026.