Healthcare

Purpose Autistic adults have diverse communication characteristics that are not well captured by standard demographic measures. Communication affects self-determination, relationships, access to healthcare, and services. Characterizing communication differences may improve research and intervention targeting for meaningful subgroups of autistic adults. Methods The Academic Autism Spectrum Partnership in Research and Education (AASPIRE) Outcomes Project team used a community based participatory research (CBPR) approach to develop a self- or caregiver-report module to characterize communication among autistic adults. The 10-item module asks about receptive and expressive communication, literacy, speech reliability, usage of digital communication, use of alternatives to speech, and communication fluency. It can be completed directly, with or without support, or via a caregiver. We used baseline survey data from 844 autistic adults to evaluate the module and derived a five-level variable, Communication Profile, from selected module items to stratify participants. We examined completion feasibility, response consistency, and construct validity. We also assessed test-retest reliability in a 15% random subsample who completed a 2-week retest following the 6-month survey, with oversampling of caregiver-report respondents. Results The Communication Characteristics module demonstrated high completion feasibility, minimal systematic error, and strong construct validity. The Communication Profile variable stratified participants into five meaningful communication profiles and demonstrated strong construct validity and substantial test-retest reliability ({kappa} = .69). Conclusion The Communication Characteristics module and Communication Profile variable provide a valid approach for characterizing communication diversity among autistic adults for research, clinical, and service settings. The full AASPIRE Measurement Toolkit is available at https://aaspire.org/measurement. Lay Summary Autistic people communicate in many different ways, and their communication may change in different situations. We developed and tested a set of survey questions to describe an individuals communication. We also used the results to describe 5 groups of autistic people based on how they communicate, without treating these groups as different types of autism.

BACKGROUND Timely cause-of-death (COD) information guides health policy, yet many deaths in low- and middle-income countries are not medically certified. In Africa, fewer than 10% of deaths are. Verbal autopsy (VA) yields a structured symptom questionnaire and a free-text narrative. Mortality surveillance increasingly records the narrative alongside the questionnaire, but the algorithms used to produce VA-based statistics were designed for structured responses only, so narratives inform physician COD assignment but do not contribute to algorithmic COD assignment. We evaluated whether supervised models could use both modalities to improve COD assignment. METHODS We analyzed 4,999 VA records for deaths at ages 12 years or older from a national sample of registered deaths in South Africa (RSA), using physician-assigned CODs as the reference standard. We trained unimodal and multimodal pipelines on the labelled records: pretrained language models (PLMs) fine-tuned on narratives alone, machine learning (ML) models on structured responses alone, and the two combined at the data, feature, and decision levels. We also prompted a locally deployed generative large language model (LLM) on the same unimodal and multimodal inputs, without parameter updates. All were compared with InSilicoVA, the operational comparator representing current automated practice. We evaluated individual-level assignment, population-level agreement, calibration, and generalization across provinces in RSA, reporting these metrics rather than ranking pipelines on any one of them. RESULTS Every supervised pipeline improved on InSilicoVA by 8.5-15.9 percentage points in top-1 accuracy and 3.5-7.1 points in cause-specific mortality fraction (CSMF) accuracy. The narrative and structured pipelines performed similarly in aggregate but differed by cause: 63.9% of deaths received identical predictions, 11.6% were correct only from the narrative pipeline, and 13.6% only from the questionnaire pipeline, with the narrative pipeline more accurate for noncommunicable causes and the questionnaire pipeline for communicable causes. Against the unimodal approaches, the decision-fusion pipelines added 4.2-5.4 percentage points in top-1 accuracy, concentrated in the most common causes, whereas feature fusion with cross-modal attention improved the less common causes, adding 6.6 points (95% CI, 1.7-11.5) in balanced accuracy and 5.5 points (95% CI, 0.6-10.3) in macro F1. Feature fusion reached the highest CSMF accuracy (95.7%). Paired differences from the better unimodal representative ranged from -0.2 to +3.4 points, with all intervals including zero. The LLM exceeded InSilicoVA on individual-level metrics and reached the highest balanced accuracy of any pipeline (60.6%), while the supervised pipelines retained higher CSMF accuracy. Supervised pipelines had lower expected calibration error than InSilicoVA. Supervised gains held across all nine held-out provinces, including every top-1 comparison and nearly every macro F1 and CSMF comparison. CONCLUSIONS PLMs using narratives alone substantially improved on current best practice and matched ML pipelines using structured responses, while multiple multimodal strategies produced further gains in individual COD assignment. The zero-shot LLM showed particular strength on rare causes, whereas supervised pipelines better recovered the population cause distribution. These findings support incorporating narratives as a core input to automated VA systems and evaluating representative fusion strategies for the intended output. External validation across settings and age groups will support broader implementation.

Background: Ambient artificial-intelligence (AI) scribes are being deployed beyond medicine into allied-health practice, but comparative evidence mainly concerns physician documentation. Objective: To compare quality and transcript-grounded clinical-risk error burden of AI-generated and allied health professional notes from identical simulated consultations. Methods: Paired simulation across dietetics, physiotherapy, occupational therapy and speech and language therapy (five consultations each; 20 pairs; 40 notes). Heidi generated an unedited first-draft note and a professional documented each encounter. Professionals rated PDQI-9 quality. Professional and automated review identified unsupported statements and omissions; two language models graded clinical risk. Co-primary outcomes were PDQI-9 score and Critical+High error density. Results: Mean PDQI-9 scores were 38.45 and 35.05 for AI and professional notes (paired difference +3.40, 95% CI +0.01 to +6.79; Wilcoxon p=0.042; dz=0.47). The Bayesian estimate was +3.47 (94% equal-tailed credible interval +0.66 to +6.30; P[AI>professional]=0.989). Automated-reviewer all-error density was 3.35 vs 8.55 per note (professional-minus-AI difference 5.20, 95% CI 3.85 to 6.55); omission density was 1.45 vs 5.20 (difference 3.75, 2.80 to 4.65). Critical+High density was 0.35 vs 0.75 (difference 0.40, -0.10 to 1.00). The pooled quality result was driven principally by dietetics. Conclusions: AI-generated notes achieved modestly higher pooled quality and fewer transcript-grounded errors, chiefly omissions; serious-error evidence was inconclusive. Small discipline samples and incomplete ground truth preclude safety or discipline-wide superiority claims.

Large language models (LLMs) are increasingly consulted for health advice, but they can produce fluent statements that are false or unsupported, known as hallucinations, and this risk is poorly characterised in low-resource languages such as Bangla. We measured hallucinations of locally deployable open-weight LLMs in Bangla diabetes question answering and tested whether retrieval-augmented generation (RAG) grounded in Bangladeshs National Guideline on Diabetes Mellitus reduces them. Forty patient questions spanning six clinical sub-domains were each answered twice, under zero-shot prompting, by Qwen3.5-9B, TigerLLM-9B, Qwen3.6-35B-A3B and Gemma-4-26B-A4B, and by BanglaDiaRAG, which adds hybrid dense (BGE-M3) and sparse (BM25) retrieval from a physician-reviewed Bangla guideline knowledge base to Qwen3.5-9B. Two physicians rated the 400 de-identified responses by consensus for medical accuracy, completeness, safety, readability and reliability on a 0-1 scale, and responses with accuracy below 0.70 were classified as hallucinated. Hallucination rates were 2.5% for Gemma-4-26B-A4B, 3.8% for TigerLLM-9B, 22.5% for Qwen3.6-35B-A3B and 77.5% for Qwen3.5-9B. Guideline grounding lowered the rate of Qwen3.5-9B to 11.3% (difference -66.3 percentage points; 95% CI -76.3 to -55.0) and raised every criterion by 0.24 to 0.38 points (Holm-adjusted p < 0.001). BanglaDiaRAG outscored Qwen3.6-35B-A3B on all five criteria and matched or exceeded TigerLLM-9B, but it remained below Gemma-4-26B-A4B in safety, readability and reliability, and its residual errors were specific, confident and potentially harmful. Guideline grounding can turn a weak general-purpose model into a much safer Bangla diabetes assistant, but the choice of base model mattered as much as retrieval and no configuration was free of hallucination, so clinical oversight remains essential before patient-facing use.

Stroke is a leading cause of mortality and disability in Uganda, with a prevalence of approximately 2.5% and a 25-30% 30-day mortality rate, exacerbated by limited access to predictive tools and advanced diagnostics. Machine learning (ML) offers potential for early stroke risk stratification, but its application in resource-constrained settings like Uganda requires tailored models and rigorous performance evaluation, as well as assessment of misclassification patterns. This study aimed to develop and internally validate an ML-based predictive model for stroke using clinical and socio-demographic characteristics among patients at Mulago National Referral Hospital (MNRH) and residents of Wakiso District, Uganda, identify key predictors and assess misclassification patterns to enhance targeted prevention. A retrospective case-control study utilized secondary data from 1,016 participants (254 stroke cases, 762 controls) across three prior studies conducted between 2006 and 2015. Eight ML algorithms were evaluated using 5-fold cross-validation, with performance metrics including accuracy, sensitivity, positive predictive value, F1-score, and area under receiver operator characteristics curve (AU-ROC). McNemars test compared model classification performance of the top 2 models. Feature importance identified key predictors, and misclassification patterns were analyzed using classification matrix and line graphs. The Random Forest (RF) model achieved the highest performance (95.18% accuracy, 90.10% F1-score, 97% AU-ROC). McNemars test confirmed RFs superior classification (p=0.01). Age, systolic blood pressure, total cholesterol, and waist-hip ratio were the top drivers of RF stroke predictions. RF misclassified 8.3% of stroke cases and 3.3% of non-stroke cases, due to overlaps in risk profiles (e.g., lower cholesterol, systolic blood pressure and age in false negatives). The RF model demonstrates strong potential for stroke risk prediction in Uganda, leveraging routine data to prioritize high-risk individuals. Misclassification analysis highlights areas for model refinement, supporting its potential for integration into clinical workflows for early intervention and stroke prevention in resource-limited settings. Future external validation across diverse populations is recommended.

This paper examines the impact of Cancer Care Ontario's regionalization of lung cancer surgery on blood transfusion use for thoracic surgery at The Ottawa Hospital. Using aggregate data scraped from the paper ''Changing trends in blood transfusion: an analysis of 244,013 hospitalizations'' by Shehata et al. (2014) we estimated the effect regionalization on the log mean number of blood transfusions among thoracic surgeons (who treat lung cancer), utilizing other surgical specialties as controls. We found that the average number of blood transfusions increased significantly among thoracic surgeons following the opening of a new cancer care centre at the hospital in December 2009, while control specialties showed no parallel change. Contextualizing these results in a modified ''population level'' treatment threshold model, it is argued that the main driver of this increase in blood transfusions among thoracic surgeons was due to the influx of less skilled thoracic surgeons to The Ottawa Hospital, and the closing of thoracic surgery units at smaller hospitals which was part of the broader regionalization process.

Objective: Current guidelines recommend different adjuvant treatment strategies for women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. We aimed to evaluate recurrence patterns and explore the clinical implications for adjuvant therapy in this population. Materials and methods: This multicenter retrospective study included 86 patients with endometrioid, p53 abnormal, FIGO stage I, II endometrial cancer treated between 2013 and 2023 at six hospitals in the Republic of Korea. Clinicopathologic data were collected through medical record review. Progression free survival (PFS) was estimated using the Kaplan Meier method, and prognostic factors were analyzed using Cox proportional hazards regression models. Results: The 12, 24, and 36 month PFS rates were 91%, 78%, and 76%, respectively. Among the 16 patients who experienced recurrence, 12 (75%) had distant metastases. Adjuvant chemotherapy showed a trend toward improved PFS compared with adjuvant radiation therapy with or without chemotherapy (p=0.10). In multivariable analysis, the p53 overexpression pattern was independently associated with worse PFS compared with the p53 null pattern (HR 8.59, 95% CI 1.61 to 45.79, p=0.01). Conclusion: Given the poor PFS, the predominance of distant recurrence and the observed trend toward improved PFS with chemotherapy, adjuvant chemotherapy may be considered in women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. Further studies are warranted to clarify the prognostic impact of p53 abnormality subtypes.

Large language models (LLMs) are increasingly evaluated for clinical tasks, but safe use requires reliable uncertainty estimates as well as high accuracy. We tested eight general-purpose LLMs on 100 validated neurology board-style multiple-choice questions and compared three confidence signals: self-reported confidence, critique-implied confidence from an independent model instance, and consistency-based confidence from repeated sampling. Accuracy varied from 45% (Llama 3.1 8B) to 92% (Gemini 3 Flash/Pro), but confidence was generally overestimated, particularly in lower-accuracy models. Consistency-based confidence provided the best discrimination between correct and incorrect answers (pooled AUROC 0.74), outperforming self-report, although calibration differences between methods were not significant. Calibration should therefore be reported alongside accuracy when clinical LLMs are evaluated.

Background: The first thousand days are a sensitive window for child health and social health inequalities. When routine care falls short, parents of young children turn to online sources and, recently, to generative artificial intelligence (AI). Evidence on their concerns, child-health literacy, and AI use is scarce. Objective: To describe health informational needs and concerns, child-health literacy, and use of AI for child-health questions, including differences by parental role and parity, among parents of young children. Methods: In this cross-sectional study, two anonymous self-administered online questionnaires were disseminated through a digital health application between May 12 and June 11, 2026, to French-speaking parents of at least one child aged 0 to 47 months in France. Questionnaire 1 covered unmet information needs, concerns across 16 topics, perceived intra-couple concordance, and confidence in routine care. Questionnaire 2 covered a four-item child-health literacy quiz and AI use (frequency, topics, 12-month trend, barriers, trust conditions, perceived effect on consultations, and disclosure). Results: In total, 12,634 parents responded (Questionnaire 1, n=5,410; Questionnaire 2, n=7,224), mostly mothers (94.6% and 92.9%). Most (61.8%) had searched for information without finding a clear, reliable answer, selecting a median of 4 (Interquartile range [IQR] 3-7) over 16 topics. Leading concerns were sleep (51.3%), nutrition (48.1%), and education (i.e., upbringing and behavior) (47.8%). Mothers were nearly twice as likely as fathers to select accident prevention (26.5% vs 16.3%) and parental mental health (38.7% vs 28.6%); first-time parents selected most topics more often than experienced parents, most markedly dental hygiene (43.8% vs 27.6%). The median child-health literacy score was 3 (IQR 3-4) on a 0-to-4 quiz, with 19.9% of parents scoring 2 or below; tooth-brushing age was the weakest item (68.4% correct). Mothers outscored fathers on every item. More than half of parents (54.2%) had used AI for a child-health question, 11.2% weekly; among those reporting a trend, 68.3% had increased AI use for child-health questions over the year and 6.2% decreased it. The main barriers to AI use were a preference for professionals (55.9%) and reliability concerns (48.7%); trust depended most on medical oversight of the tool (76.6%). AI use appeared additive, with 66.8% reporting no change in healthcare visit frequency. One third (35.5%) had told an AI something shared with neither a professional nor their network, rising to 64.1% among regular users. Conclusions: Parental informational needs were widespread and frequently unmet. AI has become a common and fast-rising source for child-health questions, at times a confidential one, used alongside rather than instead of professionals. Child-health literacy was generally high but uneven, with oral health the clearest gap. Strengths include the sample size and early-generative-AI timing; limitations include the cross-sectional design, self-report, digital-recruitment selection bias, and the small proportion of fathers.

Background Influenza vaccines have seen many changes over recent years in terms of both policy recommendations and product types. Immune responses and vaccine effectiveness vary by vaccine strain composition and product type. This study aimed to explore this variation on a regional level through examining spatial and temporal autocorrelations of vaccine uptake of different product types within Michigan from 2017 to 2021. Methods Influenza vaccine administration data from the 2017/18 to 2021/22 seasons were obtained from the Michigan Care Improvement Registry. Population size estimates for 2017 and 2019 were obtained from the Michigan Department of Health and Human Services. Age-standardized and age-stratified vaccination rates were computed. Disparate county-level vaccine uptake clusters were constructed using spatial autocorrelation indicators and spatiotemporal clusters were identified using scan statistics. Linear mixed effect models were used to study the impact of social vulnerability and urbanicity level on seasonal influenza vaccination rate. Results Vaccination incidence rates for adjuvanted, recombinant, and nasal sprays vaccines and for any vaccines increased significantly through the study period. County-level spatiotemporal clusters of type-specific uptake rates were identified. Most notably, we identified an emerging cluster with high uptake of adjuvanted vaccine radiating from Ottawa County, located in suburban Grand Rapids, and the disappearance of high uptake cluster for high-dose vaccine uptake in the area. Vaccination rates by any product type were found to vary seasonally and showed weak negative association with more rural regions. The overall vaccination rates appear to be lower among counties with higher social vulnerability in certain urbanicity levels, but social vulnerability and urbanicity were not significantly associated with overall vaccination rate during the study period. Conclusions Vaccine type-specific uptake was spatially and temporally diverse in Michigan between 2017 and 2022. Mid and Northern Michigan had consistently relatively lower vaccination rate compared to other regions in Michigan.

Background HPV-vaccination prevents HPV-related cancers, but uptake remains suboptimal. In the Netherlands, a catch-up campaign invited 18-27-year-olds for an additional cost-free opportunity to initiate or complete their HPV-vaccination in 2023-2024, which led to 24% of this invited population getting vaccinated. As young adults increasingly seek health information online, understanding online questions, concerns and misinformation in response to the campaign may help improve future vaccination efforts. Supplemental to overall evaluation of the vaccination campaign, we piloted online social listening processes to generate an insight report for Public Health Service (PHS) Amsterdam. Objective This study piloted online social listening processes at the PHS Amsterdam to identify actionable insights into questions, concerns and misinformation surrounding the HPV-vaccination catch-up campaign among young adults. Methods Upon inventory of potential platforms and data sources, a retrospective online social listening analysis was conducted across the platforms Instagram, Facebook, and X, collecting data from PHS, the National Public Health Institute for Public Health and the Environment (RIVM), the Dutch public broadcasting foundation (NOS), and LINDA.meiden sources from January 1, 2023, to June 1, 2024. Publicly available posts, comments, and engagement concerning HPV-vaccination were manually collected, and inductively coded. Themes were assessed for risk, based on expected potential harm and observed reach, and used to develop recommendations. Results A total of 821 posts and 5157 comments were collected and analysed. Ten recurring themes were identified across varying levels of risk. Two themes were classified as high risk: an information gap regarding the effectiveness of HPV-vaccination and concerns about the transparency on HPV-vaccine side effects. Three themes were classified as medium risk: questioning HPV-vaccine effectiveness under certain circumstances, reduced general vaccine trust following COVID-19, and accessibility issues related to vaccination locations and service times. Two themes were classified as neutral risk: questions regarding eligibility criteria for the campaign and calls for broader HIPV-prevention measures. Finally, three low risk themes concerned questions regarding HPV-vaccine brand specifics, campaign choices, and questions about scheduling or rescheduling HPV-appointments. Comments also referred to subsequent information sources, including reliable sources (public health institutes), false narratives and misinformation. Recommendations focused on addressing questions and information gaps (i.e. improving HPV risk perception and providing more detailed HPV-vaccine information), improving communication about HPV-vaccination, and reducing practical barriers to vaccination. Conclusions Manual social listening across selected online sources provided actionable insights into the public discourse during the HPV-vaccination catch-up campaign. Results mostly reflected questions, concerns and information gaps. Although some themes were critical of HPV-vaccination, false narratives and misinformation did not dominate the discourse. Recommendations aim to reduce practical barriers and address information needs. This pilot showed that online social listening can be effectively implemented even with limited resources.

Background. Regulatory pressure to avoid confusingly similar drug names appears to have pushed manufacturers to coin more distinctive proprietary names, but no one has quantitatively analyzed this trend. Objective. To determine whether proprietary drug names became more orthographically distinctive (unusual in spelling) over time. Methods. We examined 1,321 US Food and Drug Administration (FDA) new molecular entity (NME) names (1985-2024), 2,769 FDA Orange Book trade names (1982-2026), 177,337 US pharmaceutical trademarks (1980-2026), and 1,273 generic names (1982-2025). Compared to prior names (including those already marketed) and ordinary English, we scored each name's character trigram (three-letter sequence) surprisal (statistical unexpectedness) and its edit-distance similarity to the closest existing names. Linear regression tested time trends, controlling for name length. Results. Mean NME trigram surprisal rose in every decade between the 1980s and 2020s, from 7.21 to 8.50 nats (natural-logarithm units, higher meaning less predictable), an 18% increase (b = 0.038, p < .001, R2 = 0.34). Half of 2020s names (7% in the 1980s) contained a letter sequence found in no common English word (e.g., Jesduvroq, Omvoh). Nearest-neighbor similarity fell after the early 1990s. Trademark data showed the same trend (b = 0.016, p < .001). Generic names showed a weaker trend (b = 0.011, R2 = 0.063). Conclusions. Over four decades, drug names became more orthographically distinctive, consistent with namespace crowding, deliberate efforts to avoid confusing names, and branding strategy. Because unfamiliar letter sequences can introduce other error pathways, increased distinctiveness does not necessarily imply improved safety.

Background: Lean Healthcare has been adopted as an approach to reducing waste, enhancing process efficiency, and optimizing clinical and administrative workflows. However, empirical evaluations aimed at establishing a causal relationship between Lean Healthcare interventions and outcomes remain limited. This evidence gap is particularly pronounced in the context of large-scale implementations in low- and middle-income countries, where more efficient allocation could yield substantial improvements in population health. Methods: We conducted an impact evaluation study employing a staggered difference-in-differences approach to estimate the effects of the Lean Healthcare in Emergency Departments (ED) initiative in the Brazilian context. The intervention was implemented between January 2017 and December 2025 across 309 hospitals. We analyzed hospital-level indicators of the number of hospitalizations and the average length of stay (in days) among patients admitted through urgent or emergency pathways. Results: Our estimates suggest that the intervention was associated with an average increase of 38.6 admissions per hospital per month (p = 0.033), corresponding to approximately a 5.9% increase relative to the monthly admission volume. We also observed a reduction of 0.184 days in the average length of stay (p = 0.058), equivalent to an approximately 3.0% decrease in hospitalization time. These effects exhibited distinct temporal dynamics: the effect on the number of hospitalizations emerged gradually and increased with the duration of the intervention, whereas the reduction in length of stay became evident earlier. Conclusion: This study provides evidence of a causal effect of Lean Healthcare implementation in emergency departments on operational performance. The simultaneous increase in admissions and reduction in length of stay suggests improved bed productivity and effective capacity without expanding physical infrastructure. These findings support the use of flow-management methodologies as an effective strategy for mitigating emergency department overcrowding while increasing service capacity.

Objectives: Women's empowerment is a key determinant of health and well-being and may shape how women access and benefit from publicly-funded health insurance (PFHI). We aimed to examine whether different levels of women's empowerment were associated with 1) verification in India's PFHI Pradhan Mantri Jan Arogya Yojana (PM-JAY), 2) outpatient care utilization, and 3) resultant out-of-pocket expenditure. Design: We conducted a cross-sectional observational study. We analyzed data from a household survey, created a composite empowerment index using structural equation models, and used multivariate regression models to test associations between women's empowerment and the outcome variables. Setting: The study was conducted between December 2019 and February 2020 among PM-JAY eligible households in six Indian states (Bihar, Chhattisgarh, Gujarat, Meghalaya, Tamil Nadu, and Uttar Pradesh). Participants: 14,378 women across the six states. Results: Three main findings emerged: First, high levels of women's empowerment were associated with higher odds of completing PM-JAY verification (AOR: 1.22, 95% CI: 1.09-1.36). Second, women's empowerment was not associated with outpatient service utilization. And third, either medium levels of empowerment (coef = -0.49, p = 0.020) or PM-JAY verification (coef = -0.37, p = 0.034) was associated with lower OOPE for outpatient services. Women who had both, PM-JAY verification and medium levels of empowerment, experienced higher OOPE (coef = 0.45, p = 0.058). Conclusions: Women's empowerment is an important factor related with PM-JAY verification, but not with outpatient service use. OOPE varied depending on whether women were both empowered and PM-JAY verified or not. Our results suggest that institutional and structural barriers may play a greater role in healthcare access than individual factors pertaining to empowerment alone. Achieving equitable access therefore requires gender-responsive systems that reduce administrative barriers and support women in navigating the healthcare pathway. Our findings highlight the importance of integrating outpatient care into PM-JAY to increase financial protection, streamlining verification processes to improve women's engagement with PM-JAY and the broader health system, and advancing health financing reforms that better support women.

Background: School-based implementation of robot-assisted walking may support increased physical and social participation for children with physical disabilities, but evidence from an adequately powered trial is needed. This pilot study evaluated implementation strategies and study procedures to inform a future definitive trial. Methods: Participants were randomized to 12-weeks of robot-assisted walking or control, with subsequent crossover to the other condition. In the intervention arm, targeted dose was 75 minutes of robot-assisted walking, spread across three days/week. Implementation strategies for the child's classroom team included educational materials, centralized technical assistance, school champions, and on-going training. Implementation strategy evaluation focused on intervention protocol feasibility (i.e., weekly device use, troubleshooting) and fidelity (documentation of implementation strategy modifications). Preliminary estimates participation goal changes were obtained using the Canadian Occupational Performance Measure (COPM) 'Performance' scale (/10). Results: Eight children with neurodevelopmental physical disabilities participated (mean age 3.5, SD 0.5 years). Device use varied from 2.1 to 4.3 days/week (median 3.3) with a range of 48 to 96 minutes/week (median 80). Target use of >= 3 days/week was achieved for 66% of weeks and met the >=75-minute target for 33% of weeks. Troubleshooting occurred in 18.5% of sessions. Two implementation strategies were modified to improve feasibility. COPM mean change was 3.56 (SD 1.07) for robot-assisted walking and 2.11 (SD 2.41) for control (dz=0.74). Conclusions: This pilot study provided preliminary evidence regarding both the feasibility of robot-assisted walking implementation in schools and its potential effectiveness for children with physical disabilities. Findings support progression to a definitive trial.

Purpose: Accurately capturing a patient's health journey is essential for ensuring the validity of scientific and public health studies. Electronic health records (EHR) and administrative claims are commonly used in healthcare research, albeit separately. Previous research has demonstrated that EHR and claims capture different aspects of patient history and care, potentially introducing bias when used alone. In this study, we leveraged the largest public US EHR resource, the National Clinical Cohort Collaborative (N3C), alongside Centers for Medicare & Medicaid Services (CMS) claims data to compare their information coverage and agreement. Methods: We integrated N3C EHR and CMS data to create one cohesive dataset and quantified the proportion of key variables present in EHR alone, CMS alone, and the overlap present in both data sources. Overlap in information coverage was quantified pre- and post-landmark diagnosis of COVID-19. To evaluate the impact of data coverage differences, we conducted a case study evaluating the effect of COVID-19 vaccination on ischemic stroke using both data sources alone and in combination. Results: CMS data helped identify many individuals that were duplicated among EHR identifiers. Examination of the overlap showed that CMS data more completely captured conditions, while EHR data more completely captured medications after COVID-19 diagnosis. Some variables exhibited marked differences over time after landmark COVID-19 diagnosis. Using target trial emulation and self-controlled case series methods, we found that data capture variations altered effect estimates: EHR data alone yielded lower risk of ischemic stroke after vaccination in the target trial emulation (odds ratio 0.47, 95% CI 0.28-0.79), with a smaller, non-significant reduction in the self-controlled case series (incidence rate ratio 0.81, 95% CI 0.64-1.02), whereas analyses using CMS data alone or both sources combined yielded estimates close to the null in both designs. Conclusions: Results indicated that when using either a national database combining multiple EHR sources, or CMS claims data alone, patients' medical histories are less completely captured compared to when these data sources are combined. Case study results highlighted the value of combining data sources for not only yielding fuller patient health histories and valid scientific results, but for identifying and explaining biases that may emerge in research studies that rely on a single data source.

Background: Cost-effectiveness analyses (CEA) built on simulation models are usually delivered through project-specific scripts. Each new model, and often each new question, rebuilds the same scaffolding: parameter management, calibration, base-case analysis, sensitivity analysis and export. This is costly, and is where traceability between an estimate and the model version producing it is lost. We present THALASSA, a web application providing this infrastructure once, for any model satisfying a small programming contract. Methods: A simulation model is connected by one file implementing a fixed contract of R functions that determine its behaviour. THALASSA exposes the complete CEA workflow in a browser: a generated overview of the connected model, parameter editing, calibration, base-case comparison with the efficiency curve, and deterministic and probabilistic sensitivity analysis at a willingness-to-pay threshold, each with timestamped file export carrying the parameter values it was run with. Every session is bound to a repository version, so results carry their generating model version. Results: We demonstrate THALASSA on two public models of contrasting difficulty: a simple tutorial model and a generic cancer screening model with two competing natural-history pathways and a 21-dimensional calibration deliberately built to be more complex. Both ran on the same unmodified setup, implementing the same contract, with no code changes. Conclusions: Separating analysis infrastructure from model logic makes the CEA workflow reusable across projects and reproducible by construction. A modelling team can move from a versioned repository to audit-ready, exportable CEA outputs without writing analysis code. The integration effort does not grow with model size and one installation serves unrelated projects.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Background: Thinness-oriented body ideals are well described among young Japanese women, but variation in visual and numerical ideals by sex, age, and current BMI remains unclear. Methods: We analysed an unweighted web-panel sample of 27,359 Japanese adolescents and adults aged 15-69 years from the Survey of Body Shape and Health Awareness in Japanese People, a cross-sectional web-based survey. Current and ideal BMI were derived from self-reported height and weight, and visual orientation from ideal versus perceived current silhouette. Adult BMI-stratified analyses included participants aged 20-69 years. Findings: Among 27,359 participants (10,094 men and 17,265 women), relative thinness-oriented indicators increased with current BMI in both sexes. Among normal-weight women, 74.7-83.7% selected a smaller ideal silhouette and 79.2-91.2% reported an ideal BMI lower than current BMI. These indicators were also present among underweight women aged 20-29 years (44.9% and 43.4%, respectively), but declined with age. Most underweight men reported weight-gain-oriented ideals. Fine stratification showed that thinness-oriented ideals among women were common in low-normal and mid-normal BMI strata. Interpretation: Body-size ideals differed by sex, age, and current BMI. Thinness-oriented ideals among women extended beyond obesity and young adulthood, whereas underweight men generally preferred a larger body. Longitudinal and behavioural studies are needed. Funding: Cabinet Office of Japan Strategic Innovation Promotion Program (SIP), "Inclusive Community Platform" (grant no. SIPCP23C02).

INTRODUCTION: The digital clock drawing test (dCDT) is a cognitive screening tool capturing high-resolution pen-movement data. Existing approaches for classifying cognitive impairment from dCDTs rely on selected features or image-based methods. We propose representing dCDTs as structural networks to classify cognitive impairment and characterize spatial organization in clock drawings. METHODS: Weighted networks were constructed from 3,362 Framingham Heart Study participants, with and without cognitive impairment. Network representations were generated separately for command and copy conditions, with nodes representing pre-defined clock components (digits, hands, clock face) and edges capturing pairwise spatial relationships between components. Random forest models and graph convolutional networks were trained to predict cognitive impairment from the networks. Models were benchmarked against demographics-only and established summary feature baselines. RESULTS: Network-based models achieved AUC values comparable to the established summary feature baseline across command-only and copy-only conditions. Differences in spatial organization between the cognitively intact and impaired groups were greatest in the upper-right clock region, involving digits 1 and 2, the clock hands, and the surrounding clock face. Differences between the command and copy networks were larger among cognitively impaired participants. DISCUSSION: The proposed network representations provide an objective, scalable, and flexible framework for representing clock drawings for use in classification models. dCDT networks matched the predictive performance of established summary feature-based approaches and identified clock regions where spatial organization differed by cognitive status. After external validation, this approach has the potential to be incorporated within a clinical workflow to support automated and interpretable cognitive screening.