Annals of Internal Medicine

Heart transplantation (HT) is the durable therapy for end-stage heart failure (HF). Despite advances in immunosuppression, cardiac allograft vasculopathy (CAV) remains a leading cause of late graft failure and mortality in the modern era. Prior studies have established donor age and immunological phenomena, such as acute cellular rejection (ACR), antibody-mediated rejection (AMR), and development of donor-specific antibodies (DSAs) as risk factors for CAV. However, it remains unclear whether acute rejection (AR) that occurs early post-HT, when individuals experience the highest degree of immunosuppression, reflects higher baseline immune activity and confers a higher risk of future CAV compared to later AR, when immunosuppression is minimized. We therefore examined whether AR occurring during pre-specified early and intermediate intervals compared to those who did not experience AR in the first post-HT year was associated with future CAV among recipients without CAV at 1 year.

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

Background. The 2026 FIFA World Cup will bring an estimated 1--5~million international visitors to 11~US host cities between June~11 and July~19, 2026---the largest tournament in history. Large-scale international gatherings accelerate importation of infectious diseases from diverse source populations. Advance estimation of importation risk is essential for public health preparedness and surveillance prioritization. Methods. We developed a Poisson importation framework applied to five diseases (dengue fever, influenza, malaria, measles, and pertussis) across the 11~US venue cities. Three nested travel models of increasing resolution were constructed: a baseline model using routine June~2024 arrival data; a World Cup--adjusted model incorporating projected visitor growth factors; and a schedule-driven model routing WC fans to specific cities based on match assignments. WHO incidence and BTS T-100 routing fractions were combined with Monte Carlo uncertainty propagation (5,000 Uniform draws on under-reporting and travel-while-infectious parameters) to yield median importation estimates with 95\% uncertainty intervals. Results. Dengue posed the highest importation risk at most venue cities under the schedule-driven model (median $\Lambda > 10$ expected importations from Brazil alone; 95\% uncertainty interval 5.9--33.1), robust across the full literature-supported parameter range; Atlanta was the exception, where malaria probability exceeded dengue, driven by direct travel from West and Central African nations. Influenza ranked second at most cities, coinciding with the Southern Hemisphere winter peak. Pertussis showed broad geographic spread but carries the widest relative uncertainty, as the assumed detection rate sits at the upper bound of the literature range. Background tourism accounted for the dominant share of total importation risk; the World Cup fan increment contributed approximately 8.3\% of projected arrivals for WC-qualified nations. Conclusions. This Poisson importation framework, built entirely from publicly available data, provides reproducible importation risk estimates for mass gathering events. The framework extends to additional diseases, cities, and gatherings, offering a transparent baseline complementary to proprietary modeling systems.

Human metapneumovirus (HMPV) causes acute lower respiratory infections, primarily affecting young children and older adults, with seasonal outbreaks peaking annually in March or April in the United States and other temperate regions in the Northern hemisphere. However, the factors driving HMPV seasonality in the United States remain poorly understood. We analyzed laboratory-confirmed HMPV cases and age-specific emergency department visits across 10 US regions, fitting an age-stratified dynamic transmission model to assess spatiotemporal patterns and investigate the influence of environmental variables and viral interference from RSV on HMPV transmission rates. We found that models incorporating climate variables into the transmission rate, including vapor pressure, precipitation, potential evapotranspiration, and minimum temperature, could not capture the timing of HMPV activity across all regions. Instead, HMPV timing was associated with RSV activity, with the HMPV transmission rate reduced in the presence of RSV. We showed that, unlike RSV, only models incorporating viral interference could reproduce the biennial pattern of HMPV observed in some regions, characterized by alternating late-small and early-large epidemics. Furthermore, our model successfully reproduced post-COVID-19 HMPV and RSV epidemics and predicted that RSV interventions are not likely to lead to a substantial increase in HMPV activity despite decreasing competition from RSV. Our work unravels the spatiotemporal dynamics of HMPV and its interaction with RSV, informing future seasonal forecasting and intervention strategies for HMPV.

Accurate outbreak forecasts are critical for timely and effective public health response. In the United States, however, most forecasts are produced at the state level, which can mask substantial sub-state heterogeneity and limit their utility for local planning. We generated and evaluated forecasts of the percentage of Emergency Department visits attributable to influenza across 173 large metropolitan Health Service Areas (HSAs) using a gradient boosting quantile regression (GBQR) model, and compared their accuracy to forecasts derived from state-level data alone. At a one-week, two-week and three-week horizon, local forecasts outperformed state-based forecasts in 98.8%, 90.8%, and 78.6% of HSAs, respectively, achieving mean weighted interval scores that were on average a 39.2% lower (95% range: 5.9% to 76.7%), 19.6% lower (-6.3% to 59.5%) , and 11.4% lower (-11.7% to 44.9%), respectively. The performance advantage of local forecasting was strongest in HSAs representing a smaller share of their state's population and increased with the proportion of the HSA population living in urban areas and the number of metropolitan areas within a state. These results, based on an analysis of HSAs with populations greater than 250,000, demonstrate that fine-scale modeling can substantially improve forecast accuracy and highlight the potential value of local forecasts for outbreak preparedness and response.

Background: Most approaches to suicide risk assessment consider clinical conditions as independent risk factors, potentially overlooking prognostic information in the order in which conditions accumulate. We applied temporal sequence mining to linked claims and mortality data to identify ordered clinical diagnostic trajectories associated with suicide death. Results: The cohort included 3 647 059 insured Maryland residents aged 10 years or older with available claims records in the Maryland Suicide Data Warehouse from January 1, 2016, to December 31, 2020, among whom 768 suicide deaths were ascertained through medical examiner linkage. Sequential pattern mining of ICD-10-CM diagnoses grouped into Clinical Classifications Software Refined categories identified 89 221 candidate sequences, of which 1 816 remained significantly associated with suicide death in time-varying Cox models. Adjusted hazard ratios (AHRs) ranged from 2.4 to 134.1. Two-thirds of significant trajectories ended in physical conditions, and approximately half crossed from psychiatric to physical endpoints. Among suicide decedents, 62% were exposed to at least 1 significant sequence (median, 16 per case); median sequence duration was 18.7 months, and median time from completion to death was 13.1 months. In landmark analyses, among patients with depression who later developed suicidal ideation (n = 26 356), the path through anxiety, then anemia, was associated with higher risk (AHR, 4.6; 95% CI, 2.2-9.5), whereas the anxiety-only path was not (AHR, 1.3; 95% CI, 0.8-2.1). Among patients with anxiety who later developed hypertension (n = 149 215), the path through history of self-harm was associated with higher risk (AHR, 32.0; 95% CI, 16.6-61.6). Associations were generally consistent across sex and age. Conclusions: Temporal ordering of clinical conditions may carry prognostic information for suicide death. Clinical trajectories incorporating physical illness within psychiatric sequences identified higher-risk groups. These findings suggest that opportunities for risk detection may extend beyond psychiatric settings and that suicide risk signals may be fragmented across care settings and not apparent within isolated encounters.

During the 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo and Uganda, we projected potential airline-mediated importation risk using contemporary airline network and an externally calibrated Ebola importation hazard. Effective-distance analyses identified major international hub countries, including Belgium, France, South Africa, Kenya, and the United Arab Emirates, as higher-probability gateways within 30 days. These early projections provide a reproducible framework for real-time international situational awareness, while emphasizing that importation risk does not imply local transmission risk.

Background: Machine learning models trained on population health surveys offer scalable tools for cardiovascular screening, but recurring methodological weaknesses undermine their credibility and equity: data leakage from synthetic oversampling, qualitative rather than quantitative explainability evaluation, and the absence of demographic fairness auditing at the clinical operating threshold. Methods: We present EXHEART, a leakage-free stacked ensemble pipeline trained on BRFSS 2015 (n = 253,680) and validated on BRFSS 2020 (n = 319,795; temporal transport and retrain) and a clinical cardiovascular examination dataset (n = 68,730). The pipeline combines XGBoost, LightGBM, Random Forest, and a multi-layer perceptron as base learners with 5-fold out-of-fold logistic regression stacking and Platt scaling calibration. A quantitative SHAP-LIME consistency framework, based on Kendall-tau rank correlation and Jaccard overlap, accompanies a decision-curve analysis, a subgroup-stratified SHAP interaction analysis, and an intersectional fairness audit (Sex x Age x Income) with threshold-shifting mitigation and a frontier of the fairness-utility trade-off. The framework also adds cross-instrument fairness-disparity attribution, an empirical diagnostic that provides evidence on whether an observed subgroup disparity is more consistent with a measurement-induced or a substantive explanation by re-validating it on a dataset that measures the same clinical construct objectively. On heart disease, this diagnostic associates 89% of the sex TPR gap (95% CI [0.65, 0.99]) with the self-reported survey outcome rather than with a substantive risk difference. Results: On BRFSS 2015, EXHEART achieves AUC-ROC = 0.850, AUPRC = 0.371, Brier score = 0.071, and reduces ECE by 96% (0.256 to 0.011) via Platt scaling. Global SHAP-LIME rank agreement is moderate-to-strong (Kendall-tau = 0.580, Spearman-rho = 0.818) with a substantial top-3 divergence (Jaccard@3 = 0.200), where Stroke flips from SHAP rank 8 to LIME rank 1. The Sex TPR gap is 0.124 at the screening threshold; intersectional Sex x Age disparities reach 0.649 among adequately-powered cells, 5.2x the single-attribute gap. Temporal transport to BRFSS 2020 collapses sensitivity from 0.776 to 0.267, while retraining restores AUC = 0.840 and ECE = 0.012. On clinical examination data, the Sex TPR gap collapses to 0.014; the attribution test indicates this gap is instrument-dependent, consistent with a measurement or outcome-definition explanation rather than a substantive risk difference. Cross-domain SHAP analysis identifies four instrument-independent CVD risk factors and two major portability failures. Conclusions: EXHEART combines three practices that population-scale cardiovascular classifiers usually apply in isolation: leakage-free training with calibrated probabilities, a test of whether the model's explanations are stable, and a fairness audit that examines intersecting subgroups rather than single attributes. Bringing them together proved worthwhile. The intersectional audit revealed disparities that single-attribute auditing missed, and the cross-instrument comparison indicated that much of the sex gap reflects how the outcome is measured in survey data rather than a substantive difference in risk. The temporal transport findings indicate that deployed BRFSS models warrant periodic monitoring and retraining to maintain clinical utility. EXHEART is a retrospective methodological evaluation on public de-identified data; it is not validated for direct clinical decision-making, diagnosis, or treatment recommendation without prospective clinical validation.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

Objective: Clinical phenotyping methods that rely on clinical and informatics expertise can be time-intensive and costly. We tested both manual and highly automated approaches using electronic health record (EHR) data to identify an FDA Sentinel Initiative health outcome of interest, acute pancreatitis. Materials and Methods: We trained and evaluated machine learning algorithms using EHR data with two approaches: a custom approach that included manually curated features and trained on outcomes data validated with medical record review, and a highly automated approach that greatly simplifies and automates feature engineering and relies on low-cost silver-standard outcomes for model training. Results: Custom algorithms using manually curated structured claims data discriminated cases from non-cases with a high degree of accuracy (cv-AUC 0.89 [95%CI 0.84-0.94]); the inclusion of natural language processing (NLP)-derived covariates from clinical notes increased performance slightly (cv-AUC 0.91[95%CI 0.86-0.97]). The automated algorithm trained on the outcome count of diagnosis codes performed less well (AUC 0.80 [95% CI 0.75-0.85]) but improved using maximum lipase value as an outcome (AUC 0.88 [95% CI 0.84-0.92]). At a positive predictive value of 90%, the custom algorithm had a sensitivity of 92%, the automated algorithm trained on diagnosis code count had a sensitivity of 45%, and the automated algorithm trained on maximum lipase value had a sensitivity of 84%. However, a prediction rule derived by clinicians during chart review was nearly as accurate (maximum lipase value [≥] 3 times upper limit of normal; AUC 0.86, PPV 85%, sensitivity 92%). Discussion: Machine learning algorithms with manually curated structured data and NLP features trained on validated outcomes data successfully identified validated events. Use of an outcome in the automated model based on specific phenotype knowledge (maximum lipase value) allowed for performance similar to the custom model and with considerably less resources.

Background: The World Health Organization has expanded its recommendations for prophylactic Ebola vaccination for at-risk populations. Durable vaccine-induced immunity is important for sustaining outbreak preparedness in regions with recurrent Ebola virus disease (EVD). We assessed five-year persistence of vaccine-induced immune responses in adults and children from the PREVAC trial. Methods: Two large randomised phase 2 trials (NCT02876328), in adults and children aged [≥]1 year, were conducted in four west African countries. Participants were randomly assigned to placebo or to one of three Ebola vaccine strategies: Ad26.ZEBOV followed by MVA-BN-Filo at 56 days; rVSV{Delta}G-ZEBOV-GP followed by placebo; or rVSV{Delta}G-ZEBOV-GP followed by a homologous booster dose at 56 days. After 12 months of follow-up, the primary results were published, participants unblinded to their vaccine assignment, and follow-up continued for 60 months. After Month 24, placebo group recipients were offered active vaccination. Anti Ebola virus glycoprotein Immunoglobulin G (IgG) concentrations were measured for 5 years. Findings: 1401 adults and 1401 children were initially randomized, and 1315 (93.9%) adults and 1322 (94.4%) children attended at least one long-term visit. Retention was high, with 95% followed beyond 1 year and 83% completion at 5-year follow-up. For the three vaccine strategies, antibody geometric mean concentrations (GMC) declined modestly between Months 12 and 24, followed by a stable plateau from Months 24 to 60. At Month 60, antibody GMC were higher in the rVSV-based groups (1099 and 1216 EU/ml for adults; 1982 and 2347 EU/ml for children) than in the Ad26.ZEBOV, MVA-BN-Filo group (252 adults and 645 EU/ml children). Antibody persistence at Month 60 was heterogeneous, varying by age, sex, country, and baseline IgG concentration. Interpretation: Licensed Ebola vaccines induced sustained antibody responses in adults and children for up to 5 years. While the protective antibody level is unknown, these data demonstrate long-lasting immune responses from currently employed vaccine strategies.

OBJECTIVE: Bladder cancer (BC) is predominantly a disease of older, comorbid adults, and radical cystectomy (RC), which is the gold standard treatment, carries considerable morbidity. We sought to determine the impact of baseline dementia and frailty on the care trajectory beyond the immediate postoperative period. We hypothesized that frail patients and those with dementia undergoing RC for BC will have poorer care trajectories. METHODS AND MATERIALS: We identified Medicare beneficiaries [≥] 66 years old who underwent RC for BC in 2017 with 12 months of pre- and post-RC enrollment. Frailty and dementia were characterized using validated, claims-based measures. Associations between baseline frailty and dementia with postoperative care trajectory outcomes were determined using Fine-Gray competing risk models. RESULTS: We identified 3,600 beneficiaries of whom 11.6% were frail and 3.4% met criteria for dementia. Patients with dementia were more likely to be frail, comorbid, and not receive standard-of-care neoadjuvant chemotherapy. Frailty was independently associated with [≥] 2 transitions in care level after index discharge from RC and skilled nursing facility (SNF) admissions within 1 year of RC, exposure to intensive post-RC interventions, including dialysis and feeding tube placement, and poorer survival. Dementia remained associated with SNF admissions regardless of frailty level. CONCLUSIONS: Among a contemporary cohort of older adults undergoing RC for BC, preoperative dementia and frailty were independently associated with poorer care trajectory beyond the immediate postoperative period after RC. Our work highlights a role for preoperative geriatric assessment in identifying and optimizing patients at greatest risk.

Background Cardiovascular-kidney-metabolic (CKM) syndrome integrates adiposity, metabolic risk, kidney dysfunction, and cardiovascular disease in a prevention-oriented framework. National estimates across 1999-2023 NHANES and future burden remain limited. Methods We analyzed US adults aged 20 years from 11 NHANES cycles, 1999-2000 through August 2021-August 2023. CKM stage 0-4 was assigned using harmonized examination, laboratory, medication, and questionnaire data. Prevalence was survey-weighted and standardized to the 2010 US Census adult population. Decade trends used survey-weighted logistic regression adjusted for age, sex, and race and ethnicity. Exploratory 2040 and 2050 projections combined NHANES prevalence models with US Census projections under population-aging-only, trend-continuation, and risk-improvement scenarios. Results Among 62,890 eligible adults, 62,888 had sufficient CKM data. In 2021-2023, age-standardized prevalence was 87.9% (95% CI, 86.5%-89.4%) for CKM stage 1 and 62.0% (95% CI, 60.1%-63.8%) for stages 2-4. Stage 2 accounted for 50.1% (95% CI, 48.2%-51.9%) and stages 3-4 for 11.9% (95% CI, 11.0%-12.7%). From 1999-2000 to 2021-2023, any CKM increased by 4.6 percentage points (95% CI, 2.4 to 6.9; P<0.001), whereas stages 2-4 changed by 2.1 percentage points (95% CI, 5.1 to 0.8; P=0.156). In adjusted decade models, any CKM increased (OR, 1.28; 95% CI, 1.19-1.38; P<0.001), while stages 2-4 showed no significant linear trend (OR, 0.95; 95% CI, 0.89-1.01; P=0.084). Excess adiposity and diabetes increased, dyslipidemia declined, and hypertension, chronic kidney disease, and clinical cardiovascular disease were stable. With population aging alone, projected stages 2-4 burden rose from 164.8 million adults in 2023 to 193.7 million in 2050; under risk improvement, it was 147.7 million. Conclusions CKM syndrome remained highly prevalent among US adults. Although later stages did not increase significantly, population aging may expand the absolute care burden unless broad risk improvement occurs.

Pooled testing programs were introduced during the COVID-19 pandemic to expand surveillance capacity while preserving testing resources, but evidence on their epidemiological impact in schools under real-world conditions remains limited. We analyzed data from the pooled testing program implemented in public primary schools of the canton of Basel-Landschaft, Switzerland, during the Fall-Winter 2021 Delta wave. We used an agent-based transmission model informed by pooled and individual testing results, school characteristics, contact networks, and community incidence. The model was fitted to pooled positivity ratios in four clusters of administrative areas with similar epidemic trajectories. We compared pooled testing with alternative protocols in terms of school transmission, testing volume, and student-days lost. During the study period, pooled testing was offered to 21'187 students across 62 public primary schools, with high and stable participation across clusters (mean 71-79%). The fitted model reproduced observed pool positivity trends well. Compared with pooled testing, reactive class closure, reactive screening, and symptomatic testing were associated with higher in-school transmission, with excess ranging from 50% to 87%, 63% to 104%, and 72% to 133% across clusters. Weekly individual screening achieved similar reductions in transmission but required 15-25 times more tests. Relaxing class closure after depooling substantially reduced student-days lost without increasing transmission. Under real-world conditions, pooled testing provided an effective and resource-efficient strategy to reduce SARS-CoV-2 transmission in primary schools. Combining early detection of asymptomatic infections with low testing demands, pooled testing offers a scalable approach to school surveillance and control for pandemic response in educational settings.

Medical discrimination may alter how patients relate to health information sources following adverse care encounters. We examined whether discrimination experience is associated with selective erosion of institutional health trust and with compensatory digital health engagement, using nationally representative data from the Health Information National Trends Survey (HINTS) 6 (2022; n=6,252) and HINTS 7 (2024; n=7,278). Survey-weighted modified Poisson regression estimated prevalence ratios (PRs) for binary high-trust outcomes, and survey-weighted ordinary least squares estimated coefficients for continuous outcomes; jackknife replicate weights (50 replicates) provided variance estimates. Discrimination was associated with substantially lower probability of high trust in the healthcare system (PR=0.39; 95% CI 0.30-0.52) and physicians (PR=0.85; 95% CI 0.77-0.94), with no significant association for trust in scientists, government, family, or religious organisations. The clinical-institutional pattern replicated in HINTS 6, which additionally showed reduced trust in scientists for race/ethnicity-based discrimination. Contrary to a disengagement hypothesis, discrimination-exposed adults showed higher probability of online health information seeking (PR=1.06), health app use (PR=1.11), and online provider messaging (PR=1.13); these associations persisted after adjustment for trust in physicians. Discrimination was independently associated with lower health self-efficacy (b=-0.271). Medical discrimination selectively erodes trust in clinical institutions while leaving broader epistemic trust largely intact. Despite this, discrimination-exposed patients engage more actively with digital health channels, consistent with compensatory reorientation toward non-clinical information sources. These findings describe engaged but institutionally alienated patients, with implications for restoring clinical trust and for equity-centred digital health design.

Background Electronic health record documentation patterns may reflect workflow complexity, monitoring intensity, and operational strain in intensive care settings. However, documentation-derived features can be sensitive to local documentation culture, data capture systems, and outcome definitions. Retrospective validation across multiple datasets is therefore needed before these signals are used in workflow intelligence or clinical AI governance tools. Objective To evaluate whether documentation-density and documentation-timing features show reproducible retrospective signal for ICU workflow complexity and long-stay proxy outcomes across de-identified critical care datasets, while distinguishing workflow and long-stay associations from unsupported claims about mortality prediction, burden reduction, or deployment readiness. Methods We synthesized retrospective validation results from de-identified ICU and workflow datasets generated through a prespecified documentation-density validation program. Feature families included Documentation Burden Score style features, Shift-End Documentation Rate style features, documentation reliability style metadata, and all-documentation feature sets where available. Outcomes included long ICU length of stay proxies, mortality where available, and workflow proxy endpoints. Models compared baseline feature sets with enhanced models containing documentation-density or workflow features. Performance was summarized using area under the receiver operating characteristic curve, Brier score where reported, delta AUROC, bootstrap confidence intervals where reported, and label-shuffle controls where available. Results The strongest external long-stay proxy evidence came from the NWICU chartevents analysis, which included 28,612 ICU stays, 20,267 stays with chart events, and 9,619,759 chart events. For ICU length of stay greater than the median, baseline AUROC was 0.5252. Enhanced AUROC was 0.9512 for Documentation Burden Score features, 0.9214 for Shift-End Documentation Rate features, 0.8470 for documentation reliability style features, and 0.9517 for all documentation features. Corresponding label-shuffle enhanced AUROCs were near random, ranging from 0.4897 to 0.5064. For ICU length of stay greater than the 75th percentile, baseline AUROC was 0.5155. Enhanced AUROC was 0.9433 for Documentation Burden Score features, 0.9194 for Shift-End Documentation Rate features, 0.8118 for documentation reliability style features, and 0.9427 for all documentation features, with label-shuffle enhanced AUROCs from 0.4836 to 0.4999. Additional retrospective support was observed in eICU workflow analyses, HiRID first-24-hour documentation-density analyses, MIMIC-IV HF ICU internal analyses, MIMIC-IV-Note metadata extensions, and nursing-chart or lab density proxy analyses. However, cross-institution discrimination transfer was weak without recalibration, and several analyses remained proxy validations rather than final clinical validations. Conclusions Documentation-density and documentation-timing features show promising retrospective signal for ICU workflow complexity and long-stay proxy outcomes, especially in NWICU chartevents and selected internal dataset-specific analyses. These findings support further preregistered, prospective, silent-mode validation of documentation-derived workflow intelligence. They do not establish prospective clinical performance, mortality reduction, clinician burden reduction, autonomous deterioration prediction, or deployment readiness.

In February 2026, the FDA announced that a single pivotal phase 3 (P3) trial would become the new default standard for drug approval - a regulatory direction that had been legally enabled since the FDA Modernization Act of 1997. This announcement has strategic, scientific, and economic implications for drug developers, contract research organizations (CROs), and biotech investors. We argue that the expansion of this framework, originally reserved for various niche submissions, represents a paradigm change, dramatically increasing the value of rigorous early phase (P1 and P2) trial design, requiring sponsors to establish both statistical efficacy signals and mechanistic biological understanding before entering phase 3. Using a CNS indication cost model, we show that single P3 approval can reduce total development expenditure from approximately $447 million over 14 years to $297 million over 12 years - a savings of $150 million and providing two years of additional commercial runway for a modeled CNS drug. Case examples including lecanemab, omaveloxolone, and tofersen illustrate how biomarker-informed early phase strategies can establish the confirmatory evidence necessary for single-trial approval. We provide practical guidance for maximizing the value of P1 and P2 under this evolving framework.

Large language models (LLMs) such as ChatGPT are rapidly reshaping healthcare education and simulation-based training in non-technical skills (NTS), yet no bibliometric analysis has mapped this landscape. We searched seven open-access databases (OpenAlex, PubMed, Europe PMC, Crossref, Semantic Scholar, CORE, DOAJ) for English-language publications from January 2020 to March 2026. From 100,277 initial records, a sequential keyword funnel yielded 830 candidate papers, which were screened by 83 independent Claude Sonnet 4.6 AI agents applying pre-specified inclusion criteria (PRISMA-trAIce compliant; Cohen's kappa = 0.86 pre-reconciliation, 1.0 post-reconciliation). The final AI-verified corpus comprised 551 papers with a compound annual growth rate of 109%, contributions from 2,398 authors across 279 journals in 58 countries, and an h-index of 41. ChatGPT dominated the model landscape (46% of papers), with open-source models virtually absent. Virtual patient chatbots were the leading simulation modality (106 papers). Among NTS domains, communication (145 papers) and decision-making (135 papers) were most studied, whereas teamwork, leadership, situational awareness, and crisis resource management were markedly underrepresented. Only 6 urology-relevant papers were identified, none examining LLM integration within boot camp training formats. The field is growing at extraordinary pace but remains concentrated in a narrow range of NTS domains and a single proprietary model. Critical gaps persist in team-based skills training, open-source model evaluation, and specialty-specific simulation. AI-assisted bibliometric screening using multiple independent agents is feasible, reliable, and scalable, offering a replicable methodology for mapping fast-evolving research fields.

Objectives: Study design indexing of biomedical publications is crucial for evidence retrieval and synthesis. We sought to evaluate the accuracy and suitability of a transformer-based model (TM) for indexing clinical study designs, in comparison to National Library of Medicine (NLM) indexing. However, this is challenging for at least three reasons: First, to date, all automated systems have been trained and evaluated on manual NLM indexing assignments, itself subject to errors. Second, TM's probabilistic predictive scores take into account uncertainty, and can be converted to TRUE/FALSE assignments in different ways depending on the needs of users, while NLM labels are categorical. Third, our goal (to tag articles only that exhibit a given design) differs from NLM which tags articles that both discuss as well as exhibit that design. Materials and Methods: Therefore, we carried out a limited evaluation of the TM model that focuses only on the articles that received the most confident predictions, that is, the highest scores that are almost certainly TRUE and the lowest scores that are almost certainly FALSE, but which disagreed with NLM assignments. This was performed both for articles published in 2016 (when NLM decisions were manual) and in 2025 (when NLM decisions were automated). To establish ground truth, dual annotators indexed the articles independently, following written definitions, for four prominent study designs--cohort, case-control, cross-sectional, and case report. Results: For three designs (case-control, case report, cross-sectional), the articles having the top 100 predictive TM scores (when NLM failed to assign that design) were judged to exhibit that design in the great majority (86-100%) of cases. Conversely, the articles having the lowest 100 predictive TM scores (when NLM did assign the study design) exhibited the design only in relatively few (0-21%) of cases. The most confident predictions of the TM model were highly accurate and not redundant with automated NLM indexing; the exception was cohort studies articles, in which both TM and NLM labels showed high error rates of both omission and commission. Discussion and Conclusion: TM may have value for identifying articles exhibiting study designs, which is especially important for clinical decision-making as well as systematic reviews and other evidence syntheses. NLM indexing of cohort studies cannot be regarded as a reliable gold standard for training or evaluation of automated systems, warranting efforts to create a new manually annotated corpus.

Use of fungal mycelia, which has antiviral properties, constitutes a novel strategy for addressing existing and newly emerging viral diseases. We evaluated safety and feasibility of fungal mycelia (Fomitopsis officinalis and Trametes versicolor, FoTv) for treatment of COVID-19 and assessed its antiviral effects and potential to reduce symptoms. In a randomized, double-blind, placebo-controlled, dual site (UCSD/UCLA medical centers) clinical trial we examined non-hospitalized patients who contracted mild-to-moderate COVID-19 [&le;] 96 hours, and experienced symptom onset [&le;] nine days, before enrollment. FoTv was safe, well-tolerated, and feasible for COVID-19 treatment. Minor differences in biochemical markers were observed between groups (26 FoTv, 24 Placebo). FoTv significantly reduced the number and severity of symptoms, particularly sore throat/cough, and in vitro SARS-CoV-2 (pseudovirus) cellular infection. In conclusion, FoTv was safe and reduced COVID-19 symptoms and cellular viral infection. Future studies should investigate therapeutic benefits of fungal mycelia for SARS-CoV-2 and other viruses. Clinicaltrials.gov registration:NCT04667247.