Journal of Clinical Epidemiology

Background Late-stage cancer incidence is being considered as an earlier endpoint in cancer-screening trials, but its trial-level association with cancer-specific mortality may depend on evidence selection and endpoint harmonization. We evaluated the robustness of this association to source-verified additions. Methods We reconstructed the PubMed corpus underlying a 41-comparison review. Gemini 3.1 Pro Preview was used only to prioritize reports for blinded human reassessment. Reviewers determined eligibility, linked reports from the same trial, harmonized endpoints, and verified comparison-level data. We recalculated unweighted Pearson correlations overall and by cancer type after adding earliest-compatible trial comparisons. Results Among 1209 candidate records, 996 PDFs were assessed. Thirty-three reports absent from the source review were prioritized; 26 were eligible, representing 18 trials, and 8 provided compatible comparisons. Adding these comparisons increased the dataset from 41 to 49 and attenuated the overall correlation from 0.73 (95% confidence interval [CI] = 0.55 to 0.85) to 0.59 (95% CI = 0.37 to 0.75). Updated correlations were 0.49 (95% CI = -0.26 to 0.87) for breast, -0.23 (95% CI = -0.71 to 0.40) for colorectal, and 0.83 (95% CI = 0.54 to 0.95) for lung cancer. One sparse-event comparison influenced the colorectal estimate. Conclusions The overall association was sensitive to evidence composition, and cancer-specific stability varied. Late-stage incidence should be evaluated by cancer type and with prespecified sensitivity analyses for evidence selection and endpoint definitions. Model-assisted prioritization cannot replace human eligibility review, trial reconciliation, and source verification.

Background. Research output is counted in publications, although publications differ in how much uncertainty they resolve. An evidence delta is the change between the evidence state immediately before an index trial's results are published and the evidence state after. Whether such a comparison can be reconstructed from the published record is unknown. Methods. Six colorectal-surgery evidence lineages were chosen by the first author before any outcome data were seen, each defined by one binary patient-relevant outcome. Randomized trials of any year were sought in PubMed and PubMed Central with multiple keyword and acronym strategies. One verbatim strategy per lineage was written afterwards, re-run and archived; the records it returned that had not already been assessed were screened on title and publication type, and those that survived were assessed in duplicate. For every index trial published from 2016 through 2025 with at least one exchangeable earlier trial, the synthesis of all trials published before it was compared with the synthesis including it. The outcome was the proportion of index trials that changed a clinical decision category, defined on the risk-ratio scale with a region of no clinically important difference of 0.90 to 1.11. Categories and thresholds were fixed in an analysis plan frozen before extraction, which was not publicly registered; the interval method used in the main analysis was changed after the first run, and the frozen method is reported alongside. Study selection and data extraction were performed independently by two reviewers, with disagreements resolved by discussion and consensus, and every extracted count was verified against the source record. Results. Of 200 records assessed, 93 were eligible randomized trials and 42 reported the lineage outcome in a form recoverable per treatment group, so 41 trials entered five syntheses covering 11,817 analyzed patients. One lineage could not be analyzed at all. Twenty-nine index trials qualified. Depending on the interval method and the decision threshold, 0 to 1 of 29 index trials changed the decision category, and no trial did so in every specification in which it was included; in the amended main analysis the figure was 1 of 29 (3.4%; 95% CI, 0.6 to 17.2). Twenty-two index trials (76%), covering 8,207 analyzed patients, produced neither a category change nor a 20% gain in precision, and four trials reduced precision. Among the 14 index trials with retrievable full text, 8 cited a prior systematic review, 5 used one to justify the trial, and none presented an updated quantitative synthesis including its own result. Conclusions. Evidence deltas were reconstructable, and decision-category changes were rare and sensitive to analytic specification. The larger obstacle was reporting: 55% of eligible randomized trials could not enter such an accounting at all, because the outcome was reported without recoverable per-arm counts, reported only in another form, not measured, or reported with counts and percentages that could not be reconciled. This purposive, single-database pilot with lineage selection by the first author estimates feasibility rather than prevalence.

Importance. Observational studies dominate evidence in cardiovascular outcomes research, yet no widely adopted reporting standard requires authors to disclose how much of an observed association is attributable to systematic error. The negative-control calibration framework of the Observational Health Data Sciences and Informatics consortium returns a binary answer: a calibrated P-value either crosses 0.05 or it does not. That answer conflates "is there a signal" with "is the signal credible." Objective. To define the Bias Attribution Fraction (BAF), a continuous estimator in [0, 1] that quantifies the fraction of an observed log relative risk that is attributable to systematic bias, together with its 95% credible interval, and to evaluate whether reporting calibrated P-value plus BAF plus interval width produces more defensible decisions than reporting the calibrated P-value alone. Design. Three-part study: (i) metric definition and interval construction; (ii) Monte Carlo simulation across 1920 conditions by 1000 repetitions = 1,920,000 simulated target estimates (a primary grid plus a sign-flipped companion grid of identical size, pooled for all analyses), with truth known by construction; (iii) two-arm beta-blocker target trial emulation in MIMIC IV, adhering to the TARGET guideline, with targeted maximum likelihood estimation (TMLE) and 12 negative-control outcomes. Setting. MIMIC IV (critical care) and MIMIC IV ECG, 2008 to 2019. Participants. Adults with a first intensive care unit stay and a recorded 12-lead ECG. Exposures. Two clinical questions are evaluated: (1) reaching at least 50% of guideline-recommended target dose of an index beta-blocker within 7 days of admission, sustained to 12 months, versus lower or no exposure; (2) receiving guideline-directed medical therapy (GDMT) with at least two of three foundational drug classes at adequate dose for 12 months versus fewer. Main Outcomes and Measures. One-year all-cause mortality. Primary analytic outputs: (i) the calibrated P-value from a negative-control empirical null distribution with 12 outcomes; (ii) BAF-hat with a Markov chain Monte Carlo 95% credible interval; (iii) a verdict drawn from a truth-calibrated lookup table mapping the BAF-hat by interval-width cell to a probability of being bias-dominated. Results. Across 1,920,000 simulated repetitions, the calibrated P-value alone yielded a misuse rate (declared usable while truth was bias-dominated) of 36.02%. Adding the BAF point estimate reduced misuse to 16.48%, and requiring the whole credible interval of BAF to lie below 0.5, clear of the bias-dominated zone, reduced misuse to 10.16%. Requiring in addition a calibrated probability of bias dominance below 15% reduced misuse to 3.02% at the cost of declaring 22.1% of estimates usable (versus 61.8% for the calibrated P-value alone). Read from the bias-dominated view, meaning that withholding a repetition is a positive call and being truly bias-dominated is a positive truth, the BAF estimator achieved an area under the receiver operating characteristic curve of 0.922, compared with 0.787 for the calibrated P-value. In MIMIC IV, question 1 produced a calibrated P-value of 0.894 and BAF-hat = 0.875 (95% credible interval 0.385 to 0.992); the apparent one-year mortality reduction of 16.9% was entirely attributable to systematic bias and the association was not usable as effect evidence. Question 2 also failed layer 1, with a calibrated P-value of 0.1026, and BAF-hat = 0.460 placed it in the "competitive, no verdict" zone. Conclusions and Relevance. Pairing the calibrated P-value with BAF and its 95% credible interval turns a binary credibility judgement into a three-channel report (signal existence, effect size, and bias fraction). Despite TARGET-adherent design, doubly robust TMLE, and 12 negative controls, both clinical questions in this study still failed to qualify as effect evidence, with question 1 demonstrating complete bias domination and BAF quantifying the systematic error that remained. The reporting standard proposed here can be applied to any observational study that includes negative controls, without requiring additional data.

Objective To evaluate the accuracy and cost of RegCheck, an automated large language model (LLM)-based workflow, for identifying clinical trial outcomes and detecting outcome misreporting by comparing its outputs with manual assessments from the COMPare Trials project. Design Validation study. Setting Sixty-two clinical trials originally assessed in the COMPare Trials project, sampled from five high impact general medical journals. Participants Published clinical trial reports and their corresponding prespecified registrations and/or protocols. Main outcome measures Four prespecified research questions were examined. RQ1 assessed outcome extraction recall relative to COMPare. RQ2 assessed accuracy of outcome classification as primary, secondary, or non-prespecified. RQ3 assessed accuracy of misreporting detection relative to COMPare, with additional manual adjudication of discrepancies between RegCheck and COMPare. RQ4 assessed the average per-paper cost of running the automated workflow. Results Across the validation papers, RegCheck achieved 91.2% outcome extraction recall relative to COMPare, and 83.6% outcome classification accuracy. For detection of outcome misreporting, RegCheck's overall accuracy was 85.6%. However, after resolving discrepancies with the original human judgements (which frequently favoured RegCheck's judgement), revised accuracy for outcome misreporting detection was 94.8%. The mean cost of running the workflow was 5.94 USD per paper. Conclusions RegCheck achieved high overall performance with a rigorous manual benchmark for identifying prespecified and reported outcomes in clinical trials, and detecting outcome misreporting, while operating at very low marginal cost. Adjudication of discrepant judgements suggested that RegCheck frequently identified valid issues not captured in the reference standard. Automated outcome checking may offer a scalable way to support editors, peer reviewers, and authors in detecting outcome switching and improving trial reporting.

Objectives. To determine whether three established comorbidity scores (Charlson Comorbidity Index, Elixhauser Comorbidity Sum, van Walraven Score), applied with a single formula to both sexes, are equally well calibrated and equally discriminating in women and men, and whether any difference reaches a pre-specified threshold of clinical relevance. Design. Population-based retrospective cohort study; external validation of three pre-existing prediction models (TRIPOD Type 4) with sex-stratified evaluation. The analysis plan was fixed in the time-stamped Research Data Centre submission syntax before any aggregated data existed. Setting. Germany; complete national enumeration of acute somatic Diagnosis-Related Group (DRG)-billed adult inpatient cases, reporting years 2010 to 2024. Participants. 166 693 491 adult inpatient cases meeting a pre-specified nine-step eligibility filter. Main outcome measures. In-hospital mortality. Primary axis: calibration (calibration-in-the-large, CITL; calibration slope; observed mortality per score quintile). Secondary axis: discrimination (area under the receiver operating characteristic curve, AUC). Pre-specified thresholds: between-sex slope difference of at least 0.05, absolute AUC difference of at least 0.01. Results. Calibration-in-the-large was effectively perfect in both sexes (|CITL| [≤] 0.0004). The scores diverged in how prediction kept pace with rising risk, and in opposite directions for women and men: for the Charlson index the calibration slope was 0.968 in men and 1.034 in women (difference 0.066), with smaller differences for the Elixhauser-Sum (0.039) and the van Walraven Score (0.029). At the same score quintile, observed mortality was higher in men at most quintiles, with a maximum risk ratio of 1.21 (95 % CI 1.20 to 1.22); two van Walraven quintiles ran the other way (0.87 and 0.97). Differences in discrimination were small (random-effects AUC difference: Charlson -0.0076, 95 % CI -0.0095 to -0.0057; Elixhauser-Sum +0.0074; van Walraven -0.0026). Conclusions. Applied with a single formula to both sexes, three established comorbidity scores are calibrated differently in women and men, whereas their discrimination differs only marginally. Because these scores are reused at scale in research and in risk-adjusted mortality comparisons between hospitals, a systematic difference in calibration does not average out. This supports sex-specific recalibration. The downstream effect on hospital benchmarking is not yet quantified and should not be presumed negligible. Study registration. OSF DOI 10.17605/OSF.IO/P3QAW.

BackgroundCochrane Risk of Bias 2 (RoB 2) assessment is methodologically demanding and resource intensive. Large language models may assist this process but can produce variable judgments. AimTo develop a rule-constrained LLM system for RoB 2 assessment and evaluate its temporal stability and agreement with human reviewers. MethodsRoB2-Evaluator was developed using GPT-5.2 with fixed RoB 2 guidance and a locked instruction framework. Seven RCTs were used for calibration before version freezing. The frozen system was evaluated on 10 separate RCTs, with repeat assessment after one week and comparison against consensus judgments from two independent human reviewers. ResultsTemporal agreement was 90.0% (weighted {kappa} = 0.93), and agreement with human consensus was also 90.0% (weighted {kappa} = 0.93). Methodological rationales were stable in 94.3% of repeated comparisons. Full AI-human conceptual alignment occurred in 85.7%. ConclusionRoB2-Evaluator showed encouraging post-freeze stability, human agreement, and rationale consistency, supporting larger independent evaluations.

Background: Randomized controlled trials (RCTs) that rely on fabricated or fictitious data, commonly known as zombie trials, threaten evidence-based medicine by contaminating systematic reviews and clinical guidelines. We describe a cohort of zombie trials with confirmed data fabrication. Methods: The cohort was derived from the Retraction Watch database by updating and extending the dataset used in the VITALITY study to include all retracted zombie trials identified from database inception through December 11, 2025. To ensure that included RCTs were identified as zombie trials rather than retracted for other reasons, retraction notices were text-mined for terms indicative of data fabrication. Trial- and author-level characteristics were extracted using a combination of automated and manual methods by one reviewer. We applied descriptive statistics, coauthorship network analysis, and cascade effect mapping. Results: Among 236 retracted RCTs published between 1983 and 2024 and identified as zombie trials with confirmed data fabrication, 197 (83.5%) were associated with authors who had a history of repeated misconduct. These trials involved 450 authors overall, including only 66 unique first authors, with one author alone accounting for 104 trials (43.8% of the cohort). Authors associated with multiple zombie trials experienced substantially longer delays to retraction, with a median of 13.6 years (IQR 8.0-15.3), compared with 2.75 years for authors linked to a single zombie trial. Most retracted zombie RCTs were monocentric (91.9%), involved a median of 3.5 authors per trial, and predominantly evaluated pharmacological interventions. They were largely concentrated in anesthesiology (58.1%) and in Japan (56.4%). Adjusted for the volume of trials produced by country, Japan, Tunisia, and Egypt had the highest rates. Coauthorship networks formed fragmented, largely disconnected clusters, suggesting localized factories of fabricated evidence. Retraction cascades were common, with the identification of a single fraudulent trial often leading to chains of retractions that exposed misconduct spanning decades. Conclusion: Confirmed zombie trials were usually generated from single centers that published many such trials.

Objectives. Observational studies report large, conflicting associations between common drugs and hip or knee replacement. We tested them in the serious adverse event (SAE) tables of randomized trials on ClinicalTrials.gov. Methods. We included randomized drug trials with at least 500 participants. A language model classified arms from summaries without outcome counts (masked by instruction only). We pooled osteoarthritis-related SAEs by drug class as Mantel-Haenszel risk ratios (RRs). Because the plan followed a preliminary analysis of 72 trials, we report estimates rather than tests, checked by positive and negative controls and calibration. Results. The 881 placebo- or usual-care-controlled trials included 1.75 million participants and 9,408 osteoarthritis-related SAEs. The tables reproduced CANTOS's own canakinumab effect (RR 0.57, 95% CI 0.39 to 0.82), the anti-nerve growth factor excess (2.31, 1.82 to 2.92) and two of three modest effects specified before extraction: gallbladder disease with glucagon-like peptide-1 receptor agonists (GLP-1RAs; 1.36, 1.22 to 1.51) and ketoacidosis with sodium-glucose cotransporter-2 (SGLT2) inhibitors (1.96, 1.48 to 2.60). In the same trials, osteoarthritis-related SAEs did not differ detectably with GLP-1RAs (0.97, 0.86 to 1.10; calibrated 0.87, 0.54 to 1.41) or SGLT2 inhibitors (0.96, 0.82 to 1.12; calibrated 0.86, 0.53 to 1.40). Other proposed drugs gave imprecise estimates. Post hoc, 8 of 21 published observational estimates differed from randomized ones, in both directions (3 to 6 after calibration). Conclusions. Over about two years, GLP-1RAs and SGLT2 inhibitors had no detectable effect on osteoarthritis-related SAEs in trials that detected their known effects. Slow or modest structural effects remain possible.

Purpose: We introduce the Temporal Index of Divergent Evidence (TIDE), a metric that evaluates how the topic center of gravity of a corresponding corpus of literature aligns with, or shifts relative to, a recommendation's semantics over time. Methods: TIDE computes annual mean cosine similarity using MedCPT embedding vectors between a recommendation and corresponding PubMed records binned by year (2000-2024), testing for monotonic temporal trend (Kendall {tau}). Three recommendations with expected divergent, stable, and convergent trajectories were examined: two-stage exchange for periprosthetic joint infection (PJI), emergent fasciotomy for acute compartment syndrome (ACS), and non-operative management of distal radius fractures (DRF) in adults [≥] 65 years. The signal was characterized by an additive per-token attribution, an isolated vs. in-context analysis, and an adversarial minimal-pair test. Results: PJI diverged ({tau} = -0.84, year-shuffle p = 0.001), DRF converged ({tau} = +0.42, p = 0.005), and ACS showed no detectable trend ({tau} = -0.07, p = 0.66). The two significant directions (PJI, DRF) held under matched input [CLS] pooling, while ACS remained near zero. Publication volume baselines did not distinguish the three trajectories (publication count was universally positive, {tau} = +0.77 to +0.97). A reversed-meaning minimal-pair control showed little cosine sensitivity to reversal. Paper-level TF-IDF recovered the qualitative three-case pattern. Conclusion: Embedding-based drift analysis is a proof-of-concept candidate pre-triage signal that tracks topical alignment, not evidentiary direction; superiority over TF-IDF remains unproven. The present study is hypothesis-generating, showing potential use of embedding representations as an analytical unit rather than simply for retrieval.

Background: Large language models (LLMs) can support systematic reviews, but accurate individual outputs do not establish whether the final synthesis preserves the clinical question, accounts for statistical dependence and incorporates corrections. Objective: To develop and evaluate a framework linking LLM-assisted evidence processing to a versioned, auditable release of synthesis outputs. Methods: We used LLM agents to interpret sources and extract data. Deterministic code enforced statistical rules; investigators resolved material ambiguities and authorized release. We specified ten release properties covering evidence identities, statistical contributions and propagation of corrections. We retrospectively evaluated six integrity domains and historical failure events in one registered prognostic review, without an external comparator or held-out domain. Results: Fifty distinct root-cause events were documented, including 12 that had changed a pooled result before correction. Forty-six were resolved, and four remained disclosed limitations. The corpus comprised 454 reports, 445 studies, 441 cohort entities and 421 dependence clusters. Forty-one of 49 registered analyses were fitted, and eight retained explicit non-fitted states. All 39 source records across five principal analysis families reached a terminal source state. Two implementations within the project agreed across 1,217 numerical comparisons. All 94 file comparisons between release and publication packages were byte-identical. Two reviewers confirmed 39 principal records after seeing the same recommendations. Conclusions: This case provides a framework for inspecting synthesized evidence together with its provenance, statistical meaning and correction history. Comparative validity, generalizability and benefit in patient-centred care require independent evaluation.

BACKGROUND: Federal policy now requires sponsors to submit diversity action plans for clinical trials, yet the modifiable features of trial design associated with enrolled composition remain poorly characterized. English-language proficiency requirements are written into eligibility criteria and recorded as free text in a public registry, making them visible and, in principle, amenable to revision, but their relationship to enrolled composition has been examined only within single disease areas and in small samples of trials. We examined whether these requirements and documented language accommodation were associated with the racial and ethnic composition of enrolled participants. METHODS AND FINDINGS: We conducted a cross-sectional study of interventional trials registered on ClinicalTrials.gov with at least one US site and posted results, using a registry snapshot retrieved in October 2026. The primary cohort comprised the 44,487 trials with exclusively US sites, of which 21,924 (49.3%) reported baseline race and 17,434 (39.2%) reported baseline ethnicity with counts that reconciled with the posted denominator. Both exposures were classified from free-text eligibility criteria using regular expressions validated against independent physician review of 150 trials. For the English-language requirement classifier, sensitivity was 88.2% and specificity was 99.2%. The outcome was the trial-level proportion of enrolled participants in each category, analyzed using generalized linear models adjusted for intervention class, therapeutic area, sponsor, phase, enrollment, sites, start year, US Census region, and the population composition of the trial's site states. An English-language proficiency requirement appeared in 5,736 trials (12.9%); its prevalence rose from 7.4% of trials starting in 2010 to 26.2% in 2023 (odds ratio [OR] per decade 2.91, P<0.001), whereas documented accommodation, an eligibility statement allowing participation in a language other than English (Spanish in 99.9% of flagged trials), remained rare (788 trials, 1.8%). The requirement was associated with lower enrolled proportions of Asian (OR=0.85, 95% CI 0.77-0.93, P=0.001) and Hispanic/Latinx participants (OR=0.85, 95% CI 0.79-0.91, P<0.001), with no evidence of an association for White participants (OR=0.99, 95% CI 0.95-1.04, P=0.75). Documented accommodation was associated with a higher proportion of Hispanic/Latinx participants (OR=3.23, 95% CI 2.82-3.71, P<0.001). Geographic adjustment did not change the statistical significance of the requirement associations and attenuated the accommodation association. The main limitations were the cross-sectional design, which precludes causal inference; the restriction of enrollment analyses to trials reporting baseline demographics, which were more recent than those without such reporting; and possible misclassification of automatically classified exposures. CONCLUSIONS: English-language proficiency requirements became more than three times as common between 2010 and 2023 and were associated with lower enrolled proportions of Asian and Hispanic/Latinx participants. Less common documented language accommodation was associated with a higher enrolled proportion of Hispanic/Latinx participants. Because these criteria are investigator-written and publicly recorded, they may be a tractable target for improving enrollment equity, although whether revising them changes who enrolls requires prospective evaluation

Objectives To quantify complementary meanings of binary clinical calculator results: information beyond the observed outcome frequency, post-result risk, information direction, and precision. Design Catalogue-based cross-sectional analysis of published aggregate performance data, with a reproducible row-level audit. Setting The 847-calculator MDCalc catalogue and a fixed collection of publicly available reports assembled without a systematic literature search. Participants Evaluations comparing a binary calculator result with a binary clinical outcome using a complete or reconstructable 2 x 2 table. Main outcome measures Percentage reduction in uncertainty and corresponding information gain in bits; risk after positive and negative classifications; the proportion of information from each classification; and whether the upper 95% confidence bound for post-negative risk supported specified thresholds. Results The analysis included 482 evaluations of 407 calculators. The median study size was 422 and the median observed outcome frequency was 17.6%. Results reduced uncertainty by a median 14.9% (interquartile range 6.2%-30.5%; 95% confidence interval 12.1% to 17.2%), corresponding to 0.093 bits. In 329 evaluations (68.3%), one classification supplied more than 60% of average information. PERC reduced uncertainty by 4.3% on average while its negative classification lowered observed risk from 7.6% to 1.0%. Two HEART thresholds in the same cohort shifted the positive-classification information share from 39.1% to 79.8%. Post-negative risk was below 2% in 157 evaluations by point estimate, but the upper 95% confidence bound was below 2% in only 67; 90 of 157 (57%) did not support the apparent threshold. Conclusions Clinical calculator results have no single quantitative meaning. Conventional performance, information added beyond the observed outcome frequency, post-result risk, classification-specific information, and precision provide complementary interpretations. Whether acting on a result improves care remains a separate question.

Abstract Background Persistent pain in later life is associated with functional decline. Identifying resources associated with preserved daily function may broaden pain research beyond symptom burden alone. Objective To examine six prespecified resource indicators-physical activity, depressive symptoms, recall performance, current partner status, education and wealth-in relation to subsequent maintenance of independence in activities of daily living (ADL). Methods This longitudinal observational study analysed adults aged 50 years or older with pain at both the initial and baseline assessments in the Health and Retirement Study (HRS), the English Longitudinal Study of Ageing (ELSA) and the Survey of Health, Ageing and Retirement in Europe (SHARE). ADL maintenance was defined as no difficulty in all five prespecified ADL activities at baseline and no difficulty in all five at follow-up. Indicator-specific risk ratios were estimated using modified Poisson regression with robust variance and pooled using random-effects meta-analysis. The six meta-analysis P values were adjusted using the Holm procedure. Each indicator used a separate complete-case sample; no common six- indicators sample was constructed. Results The descriptive samples comprised 8,279 HRS person-windows contributed by 4,688 unique participants, 1,200 ELSA participants and 6,995 SHARE participants; indicator-specific denominators varied. Physical activity was associated with a higher probability of ADL maintenance across all three cohorts (pooled risk ratio, 1.145; 95% CI, 1.097-1.195; P = .005; Holm-adjusted P = .032) and was the only prespecified hypothesis to meet the Holm-adjusted criterion. The other five hypotheses did not meet this criterion; failure to do so should not be interpreted as evidence that the corresponding associations were absent. Several of these pooled estimates showed substantial heterogeneity. Conclusions Among older adults with persistent pain, physical activity was associated with a higher probability of ADL maintenance, and the physical activity hypothesis was the only one of the six prespecified hypotheses to meet the Holm-adjusted criterion in the three-cohort meta-analysis. These observational findings support further study of physical activity and functional maintenance but do not establish causal benefit.

Background: The clone-censor-weight (CCW) method is increasingly finding application in target trial emulation to compare treatment strategies involving grace periods. However, its performance has not been systematically evaluated against the ground truth. Many studies utilising CCW ignore time-varying covariates and informative pre-existing censoring. Heavy-tailed inverse probability of censoring weights (IPCW) may yield biased estimates and under-coverage. Methods: Simulation Part 1 evaluated the bias, root mean squared error, and coverage probability of CCW analysis under correctly specified and misspecified IPCW models across scenarios with or without time-varying covariates and informative pre-existing censoring. Part 2 varied the confounding strength, assessing whether inference failures could be detected using an IPCW tail-heaviness index (a weighted Hill estimator-derived Pareto-type tail index) and the estimator's standard deviation, the target of bootstrap standard error. Results: Under correct specification, all bias estimates were below 0.005; coverage approached the nominal level of 0.95 with increasing sample size. Omitting the time-varying covariate or mishandling pre-existing censoring yielded bias up to 0.069 and coverage substantially below 0.95. Under strong confounding, confidence intervals failed to attain nominal coverage, even with correct specification and large sample sizes, when the oracle tail-heaviness index was <=1. When the index was >1, the coverage approached 0.95 as the estimator's standard deviation decreased. Conclusions: CCW analysis yields accurate estimates and confidence intervals with nominal coverage when implemented correctly, and confounding is not excessively strong. Investigators should account for time-varying covariates and pre-existing censoring and evaluate the tail behaviour of IPCW distribution and estimator's bootstrap standard error.

BackgroundQuality Control Circle (QCC) reports are often reviewed qualitatively, but reviewer workload and inter-rater variability make large-scale assessment difficult. We evaluated whether multiple large language models (LLMs) could score QCC methodological quality reliably on a designed-anchor benchmark. ObjectiveTo estimate inter-model reliability for QCC quality scoring and to assess whether model scores align with designed synthetic anchors and remain descriptively comparable to a small set of public PMC QCC reports. MethodsWe evaluated 30 synthetic QCC reports and 8 public PMC QCC reports across four primary evaluators (GPT, Gemini, Grok, DeepSeek) and one sensitivity evaluator (Claude); Claude was excluded from the primary panel because it shared the model family used during prompt development. Each synthetic case was scored across eight QCC quality dimensions in three runs per evaluator. We summarized each evaluator by median scores, then estimated ICC(A,1) across the primary panel. We also examined score-based calibration against designed anchors, keyword-assisted defect mention, leave-one-out and k=5 sensitivity, and a descriptive synthetic-versus-PMC distributional plausibility check. ResultsInter-model reliability on the primary k=4 panel was excellent: ICC(A,1) = 0.953 (95% CI 0.944 to 0.962) with 237 pooled case-dimension rows. The pre-specified k=5 sensitivity analysis including Claude was 0.954, and leave-one-out estimates within the primary panel ranged from 0.950 to 0.959. Score-based calibration against designed anchors met the prespecified target in 57/58 trap-affected case-dimension rows (98.3%). Keyword-assisted defect mention was present in 51/58 trap instances (87.9%). The synthetic-versus-PMC comparison was descriptively similar across all eight dimensions, and all dimensions met the predefined descriptive margin check. ConclusionsIn this designed-anchor pilot, multi-model LLM scoring of QCC methodological quality showed high inter-model reliability and stable alignment with synthetic anchor scores. These findings support benchmark feasibility, but they do not establish expert validity, clinical validity, or operational deployment readiness.

Introduction: The TREOCAPA (Prophylactic TREatment Of the duCtus Arteriosus in Preterm infants by Acetaminophen) trial was a pan-European, multi-centre, phase II/III trial investigating the impact of prophylactic paracetamol (also known as acetaminophen) given during the first 5 days of life for children born before 29 weeks' gestational age (GA) on survival to 36 weeks' post-menstrual age without severe neonatal morbidities. However, due to time restrictions, long-term follow-up was not planned as part of the initial trial despite recommendations for neonatal trials to include endpoints measuring efficacy after discharge from the neonatal unit and on-going debate about the long-term effects of paracetamol in the neonatal period. This protocol describes a study to follow-up participants in the TREOCAPA trial at 2 years corrected age initiated after the trial's onset. Methods and analysis: The TREOCAPA Long Term (TREOCAPA-LT) study population consists of all children included in the TREOCAPA phase III trial in centres in countries participating in the follow-up study who are aged between 23.5 and 27.5 months corrected age during the study period. Families are contacted by the clinical teams and a parent or guardian is invited to complete an internet-based (online) questionnaire. The primary outcome is cognition, as measured by the non-verbal Cognitive Scale standard score from the Parent Report of Children's Abilities-Revised (PARCA-R) questionnaire. Secondary outcomes are moderate to severe neurodevelopment, health service use and costs. Data will be analysed according to the study groups to which children were randomised in the original TREOCAPA RCT. Ethics and Dissemination: The TREOCAPA-LT protocol was first approved by ethics' committees in France (the study's sponsor) and subsequently by all participating countries. Results from TREOCAPA-LT will be published in open access journals in accordance with the funder's regulations. Registration: https://clinicaltrials.gov/study/NCT06064825

Importance: The ASPREE randomized trial found no overall benefit of low-dose aspirin for disability-free survival among older adults. However, individual estimates in pre-specified subgroups indicated potential benefit among racial and ethnic minoritized participants in the United States (US). Objective: To evaluate whether the effect of low-dose aspirin vs placebo on disability-free survival differed across US Black and Hispanic ASPREE participants using individualized treatment-effect estimation. Design, Setting, and Participants: Post hoc clinical trial analysis of ASPREE, a randomized, double-blind, placebo-controlled clinical trial of daily low-dose aspirin vs placebo. This analysis included US ASPREE participants who self-identified as non-Hispanic Black or Hispanic, were aged 65 years or older, and had complete baseline predictor and outcome data. Interventions: Randomization to daily 100-mg aspirin or placebo. Main Outcomes and Measures: The primary outcome was loss of disability-free survival, defined as death, persistent physical disability, or dementia. Individualized treatment effects were estimated post hoc using a Random Survival Forest X-learner. Heterogeneity was evaluated on the relative scale with Cox proportional hazards models and on the absolute scale with 5-year risk differences. Results: Among 2411 US ASPREE participants, 1270 were included in the Black and Hispanic analytic cohort (897 non-Hispanic Black and 373 Hispanic participants; mean age, 71.8 years). Aspirin was associated with lower risk of disability-free survival loss compared with placebo (hazard ratio [HR], 0.65; 95% CI, 0.45-0.93). In model-derived tertiles, aspirin was associated with lower risk in the greatest predicted-benefit group (HR, 0.36; 95% CI, 0.19-0.71; 5-year absolute risk difference [ARD], -11.1 percentage points; 95% CI, -22.0 to -0.1) but not in the lowest predicted-benefit group (HR, 1.26; 95% CI, 0.70-2.27; ARD, +3.9 percentage points; 95% CI, -5.9 to 13.6). Conclusions and Relevance: In these analyses of US Black and Hispanic ASPREE participants, aspirin effects on disability-free survival appear to be heterogeneous, with benefit concentrated in a subset of participants. Because these findings are from post-hoc models, they should be externally validated before being incorporated into clinical decision-making. Trial Registration: ClinicalTrials.gov Identifier: NCT01038583; https://clinicaltrials.gov/study/NCT01038583

This study documents the derivation and validation of a deterministic algorithm for cause-of-death (COD) ascertainment from longitudinal real-world medical claims data, evaluated against an independent state-level death certificate file. Death certificates are the dominant reference standard in mortality research but carry well-documented limitations, including primary-cause error rates estimated at 20-40\% across empirical studies. A matched analytic cohort of 216,382 individuals (Connecticut death records, 2017--2025, age 25 and above) was constructed after exclusion of mechanism-of-injury cases and removal of ill-defined symptom-code entries from both sources. Concordance between algorithmic and certificate-based COD was assessed through three complementary frameworks: age-stratified positive predictive value (PPV) at the ICD-10-CM chapter level under a full-set concordance scenario; mean absolute rank difference (MARD) for chapters identified by both sources; and analyses of breadth, depth, and code-level specificity of COD reporting. Chapter-level PPV was strongest for individuals aged 55 and above, with all estimates representing conservative lower bounds given the known error rate of the certificate reference standard. The algorithm consistently reported broader and more granular contributing cause profiles than the death certificate, with discordances directionally consistent with the well-documented tendency of certificates to under-report contributing conditions. These findings support the conclusion that algorithmic COD ascertainment from longitudinal claims data is a feasible and scalable alternative to certificate-based attribution and, at population scale, a principled methodology for characterising death certificate error rates beyond what small-sample chart review studies can achieve.

Aims. It is unknown whether large language models (LLMs) can predict a trial's result at the design stage, from the information available when it is registered. We tested three LLMs prospectively on the Late-Breaking Science programme of the European Society of Cardiology (ESC) Congress 2026. Methods and results. Of 139 trials, 50 were selected by pre-specified criteria, and predictions were locked and registered on the Open Science Framework before the congress. Claude Fable 5, GPT-5.5 and Gemini 3.5 Flash, without web access, received each title and an investigator-compiled summary of the registered design. They gave the probability that the primary endpoint would be met, and a point estimate with an 80% prediction interval for a locked effect measure. Results were adjudicated from publications (19) or presenters' slides (31). Of 48 scorable trials, 28 (58%) were positive. The area under the receiver operating characteristic curve was 0.80 (95% CI 0.66-0.92) for Claude, 0.79 (0.66-0.91) for GPT and 0.74 (0.59-0.87) for Gemini, with accuracies of 73%, 71% and 67%. Among 26 trials whose effect was reported exactly as locked, the direction was correct in 88% of predictions and the median multiplicative error of ratio estimates was about 15%. The 80% prediction intervals contained the observed effect in 73.1% (Claude), 84.6% (GPT) and 57.7% (Gemini). Conclusion. From the registered design alone, LLMs showed statistically significant discrimination between positive and negative trials and usually predicted the direction and approximate size of the effect. Such predictions might help trial design, and further studies are needed.

Background. Hypermobile Ehlers-Danlos syndrome (hEDS), postural orthostatic tachycardia syndrome (POTS) and mast cell activation syndrome (MCAS) are reported to co-occur frequently. It is unclear how strongly, whether symptom profiles sharpen prediction of a second diagnosis given a first, and whether the published literature can support such inference at all. Methods. We pooled 22 published cohorts-aggregate prevalence data, no primary human-subjects data-using Bayesian hierarchical random-effects models on the logit scale, and propagated the resulting posteriors through naive and tempered symptom updating. We introduce a feasibility screen derived from the Frechet-Hoeffding bounds that tests whether separately pooled marginals can describe a single population, and we characterise the identifiability of latent class structure under disease-selected sampling. Results. Directed comorbidity is strongly asymmetric: {pi}POTS|hEDS = 46.6% (95% CrI [32.5,61.5]) against {pi}hEDS|POTS = 12.1% ([3.5,38.5]), a near-fourfold gap, with {pi}MCAS|POTS lowest at 3.9% ([0.7,21.6]). Prediction intervals exceed credible intervals throughout, indicating substantial between-cohort heterogeneity. The feasibility screen finds 26 of 102 testable cells (25.5%) incompatible with any joint distribution; critically, 21 of these fail the upper Frechet bound and are invisible to the one-sided screen that is the natural first implementation. Among cells surviving the screen, symptom evidence is informative in four of six directions-P(hEDS | POTS,S) rises from 12.1% to 49.7% on a four-symptom panel under tempered updating, and P(POTS | MCAS,S) from 49.5% to 82.1%-but inert in both hEDS-cohort directions. A pathway-dispersion contrast excludes zero in two of six directions, in opposite signs and by margins of 0.1-0.2 percentage points, consistent with chance at this number of comparisons. We show latent class structure is not identified from disease-selected aggregate data, and that the single cohort reporting trivariate structure (N = 8) yields an exactly balanced table (OR = 1.00, 95% CI [0.063, 15.99]). Conclusions. The pooled directional probabilities are usable as clinical priors, with intervals wide enough to preclude precision. Symptom-conditioned prediction is supported in some directions but not those most often invoked clinically, and every estimate rests on cohorts dominated by self-reported ascertainment. The principal methodological contribution is the two-sided feasibility screen: applied here it shows that a quarter of the testable literature cannot describe one coherent population, and that a one-sided implementation understates this sixfold. Keywords: hypermobile Ehlers-Danlos syndrome; postural orthostatic tachycardia syndrome; mast cell activation syndrome; comorbidity; Bayesian meta-analysis; random-effects model; Frechet bounds; identifiability; latent class analysis; collider bias