Clinical Trials

Background: Adolescent substance-use rehabilitation is a care-continuum problem spanning detection, engagement, active treatment, relapse prevention, aftercare, family support, and equity-oriented implementation. Existing reviews are often modality-specific and do not show how evidence aligns with substances, populations, outcomes, stages of care, or policy needs. Objectives: To map and synthesise the 2015-2025 adolescent and transitional-age youth SUD rehabilitation literature across intervention domains, stages, substances, outcomes, equity/disadvantage, geography, and economics, and to perform meta-analysis only where pooling was clinically defensible. Methods: PubMed, Scopus, and Web of Science records were harmonised to 2015-2025 and deduplicated. Two reviewer roles applied a predefined charting codebook for substance focus, technique family, rehabilitation stage, equity/disadvantage flags, outcome family, and study-design signal. Evidence was synthesised across AI/digital, psychiatric/psychotherapeutic, pharmacological, family/social, behavioural, residential/continuing-care, school/community, harm-reduction, and policy domains. Random-effects meta-analysis was restricted to comparative youth OUD medication-supported trials with extractable binary outcomes. Results: The search identified 1,676 records; 554 duplicates were removed, leaving 1,122 unique records. Metadata screening retained 579 records for evidence-map charting: 112 high-confidence records and 467 conservative metadata-supported records requiring full-text verification before final selective-journal submission. The charted evidence was concentrated in active treatment (n=433) and relapse prevention (n=114); aftercare/follow-up was weak (n=8). Intervention-family signals were led by pharmacological/MOUD (n=72), psychotherapy/psychiatric care (n=65), school/community/brief interventions (n=46), residential/continuing care (n=41), family/social therapy (n=30), AI/digital/telehealth (n=25), harm-reduction/policy (n=24), and CM (n=22). The primary youth OUD retention/completion meta-analysis favoured medication-supported treatment (OR 7.67, 95% CI 3.98-14.78; I^2=0%; k=2; n=188). An exploratory favourable-outcome analysis produced a similar estimate (OR 7.94, 95% CI 4.24-14.89; I^2=0%; k=3; n=229). Conclusions: The strongest pooled quantitative claim supports medication-supported treatment for youth OUD. For non-opioid substances, digital care, family therapy, CM, residential care, aftercare, and equity-oriented implementation, the literature is clinically important but not yet consistently synthesis-ready. Future trials should evaluate complete care pathways, adopt core outcomes, report age-banded and equity subgroup effects, and include economic and implementation endpoints.

Meta-analysis usually reduces each study to an effect estimate with a standard error and pools these by inverse-variance weighting: fixed effect (FE), random effects (RE), or unrestricted weighted least squares (UWLS). We propose information-geometric meta-integration (IGMI), representing each study by its sampling distribution, the Gaussian N(theta_i, Sigma_i), and pooling studies as a weighted Frechet mean (barycenter) under Bures-Wasserstein (BW), Fisher-Rao, or Wasserstein-Fisher-Rao (WFR) geometry. In the scalar fixed-variance case the BW barycenter mean is exactly the FE estimate; the minimized Frechet functional reproduces the Higgins-Thompson I^2 and DerSimonian-Laird tau^2 heterogeneity statistics; and a Frechet-scatter pivot reproduces the Hartung-Knapp-Sidik-Jonkman interval at m = 1 and yields an exact Hotelling F(m, K-m) region for m outcomes under proportional total covariances. WFR adds a robust outlier-resistant pool: as its length scale delta grows without bound it converges monotonically to BW, whereas finite delta gives a redescending M-estimator with rejection point exactly pi*delta. Simulations show calibrated multivariate coverage at small K, where Wald intervals undercover, and strong resistance of the equal-weight WFR pool to contamination. In 2,445 Cochrane meta-analyses, WFR most often wins leave-one-out predictive scoring. In 835 bivariate meta-analyses, the closed-form BW barycenter matches REML multivariate meta-analysis predictively and is exactly invariant to the unreported within-study correlation, unlike the likelihood estimate.

Objectives. To present DrugSet, a validated R Shiny application supporting the construction medicinal products codelists based on the Anatomical Therapeutic Chemical (ATC) system and their mapping to Clinical Practice Research Datalink (CPRD) Aurum prodcodes within a single interactive workflow. Materials and Methods. DrugSet comprises four modules: data preparation, ATC-based hierarchical code selection, string-based CPRD Aurum prodcodes mapping, and codelist export. Validation was conducted against World Health Organization (WHO) ATC reference codelists and manually curated prodcodes mappings across three drug classes: metformin, beta-blocking agents, and topical salicylic acid. Sensitivity, specificity, and Positive Predictive Values (PPV) were calculated for ATC codelist generation. Agreement proportions (overlapping against total identified codes) were calculated for prodcodes mapping. Time needed for codelist construction using DrugSet was recorded and compared to manual approaches. Results. DrugSet ATC codelist generation against WHO manual reference achieved 100% sensitivity, specificity, and PPV across all medicinal products. Prodcodes mapping agreement ranged from 89.2% to 98.3% with discrepancies due to missing data in the prodcodes input vocabulary. DrugSet completed codelist construction in 9 minutes compared to 3 hours and 10 minutes manually, across all medicinal products classes. Discussion. DrugSet provides a unified workflow that runs directly on ATC and source CPRD Aurum vocabulary files. The reduction in codelist construction time and export of the generated codelists supports reproducibility in pharmacoepidemiologic studies where codelist creation can represent a significant proportion of study setup time. Conclusion. DrugSet is an open-source, validated tool that improves accuracy, and efficiency of codelist construction for medicinal products based on ATC codes towards CPRD Aurum prodcodes.

Background: The UK Government aims to reduce smoking rates by implementing new, and investing in existing, tobacco control strategies including increased funding for Stop Smoking Services (SSS) in England. This study examined whether the additional funding starting in April 2024 was associated with a detectable increase in quit attempts supported by SSS and whether it was cost-effective. Methods: We used data from the Smoking Toolkit Study, a repeat cross-sectional survey conducted in 2021 to 2025. Adults aged [≥]18 years who smoked cigarettes and had made a quit attempt in the past year were included (weighted n=5,076). The outcome was monthly prevalence of past-year quit attempts supported by SSS. We fitted general additive models with a step change in April 2024 to represent the start of the increased funding. We adjusted for tobacco tax increases, the Swap-to-Stop scheme, age, gender, and a measure of socioeconomic position. In an unplanned analysis, we extended the time series back to 2006. For the cost-effectiveness, we estimated incremental cost-effectiveness ratios for the total population and age groups, accounting for future lifetime cessation. Results: In the primary model, the April 2024 step change was not statistically significant (adjusted odds ratio: 1.13; 95% CI: 0.52, 2.49). The cost-effectiveness analysis ranged from cost-effective to extremely ineffective (incremental cost-effectiveness ratio (ICER): GBP 104,126, 95% CI: 939,398 to 8,293). When using the extended time series, the adjusted odds ratio for the step change was 2.70 (95% CI: 2.03, 3.60) and the intervention was cost-effective (ICER: GBP 13,857; 21,393 to 9,620). Conclusions: Compared with the long-term trend, increased funding to SSS in England in 2024 appeared to lead to an increase in quit attempts supported by SSS at the population level. This result is somewhat uncertain because our primary pre-planned analyses assessing the impact relative to a more recent trend were insensitive.

Introduction: Primary care clinicians (PCC) commonly make decisions in the context of time delay and uncertainty. Delay discounting (DD) and probability discounting (PD) are cognitive biases related to delay and uncertainty that are minimally explored in PCC. We assessed DD and PD in PCC and evaluated their association with low-value care (LVC) decision-making. Methods: We administered a survey to PCC in a Southeastern U.S health system and within the American Academy of Family Physicians networks. The survey comprised standardized psychometric assessments of DD and PD and four LVC clinical vignettes. Outcomes included DD and PD discounting rates for two monetary rewards ($100 and $10,000) and ratings of LVC likelihood (0-100). We used regression analysis with model selection to evaluate the relationship between variables. Results: 225 PCC (89% physicians, 11% advanced practice providers) participated. Heterogeneity in DD and PD rates was observed. For the $10,000 reward, ln k(DD)= -6.80, IQR:-7.60--6.10) and ln h(PD)= 1.75, IQR:1.75-2.36). The reward amount impacted DD and PD in opposing directions (i.e., lower DD/higher PD rates for $10,000 vs. $100). LVC likelihood was highest for low-value antibiotics and lowest for low-value cervical cancer screening (median 20, IQR:10-40 and 0, IQR:0-10, respectively). Model selection revealed demographic associations with LVC likelihood, but no association with DD or PD. Conclusions: Consistent with effects previously reported in non-clinicians, PCC exhibited a range of DD and PD, which ranged by reward magnitude. Neither DD nor PD predicted vignette-based LVC likelihood. Further research should investigate actual clinical practice patterns and other LVC scenarios.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been linked to heterogeneous, potentially pleiotropic effects across organ systems, motivating outcome-wide comparative risk profiling in real-world data. A central challenge in such analyses is \emph{residual bias} that remains after adjustment for observed confounders, which can distort effect estimates and mis-calibrate uncertainty. We present distributional diagnosis and calibration (DC), which uses panels of negative control outcomes (NCOs) to diagnose residual bias and calibrate uncertainty. DC evaluates null behavior via $p$-value uniformity and empirical coverage across NCOs, and uses the empirical distribution of NCO effect estimates to calibrate confidence intervals for prespecified primary outcomes. DC is modular: it can wrap around commonly used causal inference methods and operates directly on summary statistics, supporting collaborative research under data-sharing constraints. Using electronic health records from a large U.S. clinical research network (152.7 million patients), we compared GLP-1RAs with sodium--glucose cotransporter~2 inhibitors across 15 prespecified outcomes spanning cardiovascular, mental health, and genitourinary domains using four causal estimators. Across outcomes and methods, DC diagnostics revealed substantial and method-dependent residual systematic error. DC calibration attenuated systematic error signals observed in negative controls and yielded more stable, better-calibrated estimates for clinical outcomes, supporting DC as a practical strategy to strengthen the credibility of real-world comparative effectiveness research.

Clinical informatics pipelines increasingly compute validated clinical endpoints from upstream NLP outputs. Even when the endpoint is defined by an established rubric, translating that rubric across representations - natural language instructions, program logic, and reference implementations - can introduce specification drift, where ostensibly equivalent calculators yield meaningfully different scores. We study this phenomenon for the Expanded Disability Status Scale (EDSS), a standard measure of disability in multiple sclerosis. Holding constant a shared set of functional system (FS) subscores extracted by a large language model (LLM), we compare EDSS values computed across three representations of the same scoring rubric: prompt-executed natural language, LLM-generated code, and a canonical reference implementation. We characterize disagreement structure, distributional shifts, and clinically salient boundary flips, and we propose an audit workflow that treats endpoint computation as a first-class verification target in clinical NLP systems.

The rapid expansion of the medical artificial intelligence (AI) literature has outpaced our ability to judge how far published models have progressed towards clinical use. We investigated whether the translational maturity of a study can be estimated automatically from its abstract. Using PubMed, we assembled a corpus of 11,024 candidate articles, reduced it to 1,816 AI-related articles by heuristic filtering, and manually double-annotated a balanced sample of 524 articles across five maturity classes (internal validation, external validation, prospective evaluation, implementation or governance, and not applicable). Abstracts were represented as TF-IDF features and classified using multinomial logistic regression with a Lasso penalty, chosen for interpretability and suitability for a small, imbalanced dataset. On a stratified held-out test set (n = 104), the model achieved 69.2% accuracy, Cohen's kappa of 0.495, macro-F1 of 0.458 and a weighted AUC of 0.820. Performance was strong for the frequent classes but poor for the rare implementation or governance class, which the model failed to recover. A balanced manual verification of 200 large-corpus predictions confirmed this pattern, with per-class precision ranging from 82.5% (internal validation) to 5.0% (implementation or governance). An interpretable, low-resource classifier can support literature mapping but requires human oversight for advanced maturity levels.

Objective: Online health information seeking is rising, and individuals increasingly act on peer advice without clinical oversight, adjusting doses, delaying care, and modifying treatment. Current misinformation detection assumes factually inaccurate content is what makes these decisions unsafe. We introduce VERITAS (Verification Engine for Risk-aware Information Trust Assessment in health Stories) and formalize the Risk Irrelevance Principle: divergence from accepted clinical practice and potential for harm are distinct, weakly associated dimensions that must be assessed separately. Materials and Methods: VERITAS transforms unstructured health narratives into Agent-Action-Outcome graphs and computes two continuous metrics: Narrative Truth Distance (NTD), quantifying epistemic divergence, and Narrative Risk Score (NRS), assessing harm potential. We evaluated VERITAS on 704 threads from four Reddit health communities. Two domain experts annotated 2,000 segments (Krippendorffs =0.78-0.81). NTD-NRS independence was validated using seven tests. Results: NTD and NRS shared under 5% of variance (r = 0.222; mutual information 0.096 bits): a posts divergence from consensus conveys little about whether acting on it will cause harm. On 435 labeled posts, VERITAS identified 62.2% of expert-labeled misinformation versus 57.5% for the strongest text classifier, the gain concentrated in factually plausible content describing unsafe self-management (27.6% of misinformation) that accuracy-focused classifiers approve. VERITAS assessed 37.8% of this misinformation as low-risk, pending clinical validation. Discussion: Fact-checking-based screening systematically approves the content most likely to prompt unsafe self-management while flagging content least likely to cause harm. Conclusion: Separating divergence from harm potential shifts verification from whether information is correct to whether it is safe to act upon.

Abstract Climate change is altering environmental conditions that influence foodborne disease transmission, yet traditional systematic reviews cannot keep pace with expanding evidence. We assessed whether an LLM-assisted workflow could generate a rapid, repeatable, and policy-relevant living evidence base for climate-sensitive foodborne disease. We combined structured PubMed searches (2010-2023), gold-standard human labelling, and iterative refinement of a GPT?4?Turbo?based auto-labeller within the SysRev platform. Pathogens of public-health importance in England were selected a priori. Model performance was evaluated against human reviewers using recall, precision, specificity, accuracy, and balanced accuracy. The refined inclusion model achieved 89{middle dot}2% recall, 59{middle dot}2% precision, 84{middle dot}5% specificity, and 85{middle dot}4% accuracy across 1,044 screened abstracts, identifying 436 studies for inclusion. Post-hoc re-evaluation of discordant abstracts showed that records excluded by the model but included during initial human screening did not meet the refined inclusion criteria. Frequently identified climate exposures included rainfall, temperature, seasonality, and humidity; norovirus, Salmonella, Campylobacter, and Cryptosporidium were the most common pathogens. An LLM-assisted workflow can generate living evidence for climate-sensitive foodborne disease with high recall and improved screening consistency. The approach is scalable, auditable, and suitable for secure institutional environments, supporting horizon scanning and climate-health risk assessment.

Background Clinical decision support systems configured as electronic medical records alerts can support clinical trial recruitment by identifying eligible participants. While they are typically used to evaluate a single patient record, their application for a linked maternal infant chart has not been extensively explored. Objective This project aimed to design and implement a clinical trial alert using linked maternal infant records and to evaluate researchers perceived usability. Materials and Methods We conducted a two phase quality assurance project: (1) design and implementation of an alert aligned with the anticipated recruitment workflow, and (2) evaluation of usability using the System Usability Scale. Basic content analysis described the alert design and implementation processes, while quantitative scoring assessed perceived usability. Results Over a 12 month period, only one alert was triggered due to changes in the recruitment workflow. Two silent alerts assessed maternal eligibility in outpatient and infant eligibility in inpatient settings. Three of four researchers completed the survey, yielding a score of 92.5, indicating excellent usability. Conclusion Although the alert was technically functional and perceived to have excellent usability, its performance was limited by deviations from the intended recruitment workflow. Researcher engagement and recruitment workflow alignment emerged as critical factors influencing alert utility. Clinical trial alerts for linked maternal infant records can be designed and implemented with excellent usability; however, consistent adherence to the recruitment workflow is essential. Broader application in additional study settings is recommended.

Background: Sphingosine 1-phosphate receptor (S1PR) modulators can cause transient, dose-related negative chronotropic effects. Mocravimod is an oral S1PR modulator that is developed as a maintenance therapy in allogenic haematopoietic cell transplantation (allo-HCT). This phase I study evaluated whether two dose-titration regimens attenuate early bradycardia when initiating mocravimod while preserving pharmacokinetic (PK) and pharmacodynamic (PD) activity. Patients and methods: In this randomized, double-blind, placebo-controlled, parallel-group study, healthy adults received once-daily oral mocravimod using either dose titration (DT) regimen DT1 (0.3-2.0 mg with 4-day stepwise escalation) or regimen DT2 (0.5 mg to Day 14, 1.2 mg Days 15-18, then 2 mg), a fixed 2 mg regimen, or placebo for 21 days. The primary endpoint was the number of bradycardia episodes on treatment initiation and dose-escalation days derived from 24-hour Holter monitoring; PK of mocravimod and mocravimod-phosphate (whole blood) and PD effects (absolute lymphocyte count [ALC]) were assessed. Results: Fifty-six participants were randomized and 53 completed the study. Both titration regimens resulted in fewer bradycardia episodes than fixed initiation at 2 mg during the first week of treatment. Differences between titration and fixed dosing were no longer evident after Day 9, consistent with tolerance development. PK profiles were consistent with prior phase I data. By Day 21, DT1 achieved exposures close to the fixed 2 mg regimen, whereas DT2 yielded lower exposures, reflecting slower escalation. Peripheral lymphopenia developed in all active treatment groups and was comparable between regimens by Day 21, returning toward baseline by study end. Safety was similar between titration regimens and placebo, with similar distribution and incidence of adverse events. No serious adverse events occurred. Conclusion: Two practical titration regimens mitigated the early negative chronotropic effect observed with fixed-dose initiation of mocravimod at 2 mg once daily. Importantly, titration preserved the expected PK and PD profile, supporting dose escalation as an effective initiation strategy to improve early cardiac tolerability.

Caregivers increasingly act as proxies, managing patients digital accounts and making complex end-of-life decisions. Greater dyadic engagement in advance care planning (ACP) improves patient and caregiver outcomes, yet empirical evidence linking formal digital proxy roles to ACP engagement remains limited. The study aims to quantify patterns of ACP engagement, digital proxy roles, and how these caregivers behaviors are associated among caregivers in Singapore. We conducted a cross-sectional survey among an online panel of nationally representative adults in Singapore to identify caregivers and assessed their lifetime engagement in formal proxy roles across legal, financial, and medical digital domains, along with ACP proxy behaviors. Formal digital proxies had institutional or joint access to digital financial accounts (for financial digital proxies) or digital patient health/caregiver accounts (for medical digital proxies). ACP engagement was measured using 13 proxy-related behaviors, such as discussing end-of-life care preferences. Multivariable regressions were performed. In total, we identified 276 caregivers, who assisted with instrumental activities daily living to another adult from 311 completed responses. Among caregivers (age 41.0{+/-}13.8, 46.2% female), 28.9% were legal proxies and 40.2% were formal digital proxies (31.5% financial; 29.0% medical). Overall engagement was modest (mean 3.97{+/-}4.54) despite most reported completing at least one behavior. Compared to non-proxies, medical (AME=3.722, 95%CI: 2.143-5.301) and financial digital proxies (AME=1.515, 95%CI: 0.121-2.910) reported significantly higher ACP engagement while legal proxy status did not. High-stakes discussions on life-sustaining treatment and health-state preferences showed low engagement. Formal digital proxy roles are positively associated with ACP engagement and may provide a strategic entry point for interventions. Persistent deficits in high-stakes ACP highlight limited readiness for complex end-of-life decisions and the need for targeted decision-support tools.

Importance: Adverse drug events in older adults are a substantial public health burden, yet spontaneous reporting systems detect them poorly owing to underreporting and the lack of a defined population. These limitations are of particular concern for older adults, who are underrepresented in pre-approval trials yet at elevated risk owing to polypharmacy, multimorbidity, and age-related changes in drug metabolism. Objective: To develop and apply an active, claims-based pharmacovigilance framework using sequential target trial emulation to detect adverse drug event signals in older adults, with atorvastatin as the initial application. Methods: Using Medicare fee-for-service claims (2017-2019), we studied statin-naive beneficiaries aged 65 years or older following myocardial or cerebral infarction. We emulated up to 14 daily sequential trials from the discharge date, classifying patients as initiating atorvastatin (A1), initiating a different medication (A2), or no new medication (A0); the primary contrast was A1 versus A2. For each trial, incident outcomes were ascertained and classified into 552 outcomes based on the Clinical Classifications Software Refined categories. Per-protocol effects were estimated over a 6-month follow-up period using Fine-Gray regression models weighted by the inverse probability of treatment and censoring, treating death as a competing risk, with the false discovery rate controlled via the Benjamini-Hochberg procedure. A signal was declared when the q-value was 0.10 or lower and the subdistribution hazard ratio (sHR) was 1.20 or greater in any prespecified analytic stratum (sensitivity analyses used thresholds of q 0.20 or lower and sHR 1.20 or greater). Results: Of 70,130 eligible patients, 39,948 initiated atorvastatin (A1) and 19,182 initiated another new medication (A2); after weighting, baseline characteristics were closely balanced. After excluding outcomes with sparse cell counts, 295 outcomes were analyzed; five met the primary signal detection criteria: valve disorders (sHR 1.71, 1.20 to 2.43); sprains and strains (sHR 1.79, 1.26 to 2.54); general sensation/perception symptoms (sHR 1.23, 95 percent CI 1.11 to 1.36); abnormal findings without diagnosis (sHR 1.55, 1.18 to 2.05); and prediabetes (sHR 1.71, 1.24 to 2.36). In the sensitivity analysis, we additionally detected posthemorrhagic anemia, hemorrhagic stroke, varicose veins, and other circulatory and skin conditions. Conclusions: An active, claims-based framework using sequential target trial emulation detected both expected and previously unrecognized adverse drug event signals following atorvastatin initiation in older adults, offering a systematic alternative to passive surveillance that can be extended to other commonly prescribed medications.

Introduction: For oncology patients with limited treatment options, clinical trials may be a critical lifesaving pathway. Identifying relevant trials, however, is a time-consuming and difficult task. Several patient-trial matching processes incorporating large language models (LLMs) have been proposed to alleviate the burden on patients and oncologists. We aim to explore the benefits and practical challenges of zero-shot LLM-assisted trial matching processes by analyzing the results for a single pancreatic cancer patient. Materials and Methods: The results of a simple zero-shot LLM-assisted clinical trial matching process for our patient were compared to those of a "human benchmark," which was developed manually by two of the authors interfacing directly with ClinicalTrials.gov. Performance metrics -- sensitivity, specificity, precision, and accuracy -- were calculated. In addition, a qualitative content analysis (QCA) of LLM reasoning text was done to identify patterns in "errors," which we define as a human-LLM discrepancy in final patient eligibility. Implications and severity of errors are discussed. Results: The zero-shot LLM-assisted process returned potential trials with a sensitivity, specificity, and precision of 81.1%, 89.3%, and 86.5% respectively compared to the human benchmark. Qualitative error analyses revealed that about 73% of errors could potentially be alleviated with improved prompting and information access. Overall performance seemed comparable to that of human reviewers. Conclusion: The results from this preliminary real-world case study provide additional evidence to the literature in support of the integration of LLMs in clinical trial matching to provide benefit to patients with metastatic cancer with limited options.

Adolescent use of alcohol, nicotine, and marijuana remains a major public health concern in the United States. Early identification of youth at elevated risk is critical for prevention before use begins or escalates. We developed and evaluated a longitudinal machine learning framework to predict alcohol, nicotine, and marijuana use at the next observed assessment wave. Data came from the Adolescent Brain Cognitive Development (ABCD) Study Release 6.0. The models incorporated predictors from multiple domains, including demographics, friends, family and community context, mental health, physical health, and prior substance-related behaviors. To reduce information leakage across individuals, we implemented a leakage-aware stacked ensemble. This ensemble combined diverse base learners through out-of-fold predictions and an elastic-net meta-learner. Across all three substances, the lagged stacked ensemble outperformed the cross-sectional stack and all single base learners. Adolescents identified as highest risk showed substantially higher observed rates of substance use than would be expected under random screening. Feature-importance analyses showed that the full longitudinal models were strongly influenced by developmental timing and prior-use history. Analyses restricted to current-wave features revealed distinct substance-specific risk patterns beyond prior-use history and developmental timing. Bootstrap stability analyses identified top-ranked features showing consistent positive predictive relevance across resampled adolescents. These findings suggest that longitudinal, leakage-aware machine learning can generate substance-specific risk estimates to support targeted prevention and screening in adolescent populations.

Systematic reviews in computational biology require screening large heterogeneous bibliographic sets, especially when topics span computational methods, cancer genomics and statistical modelling. This paper presents a reproducible semantic triage pipeline that combines SPECTER scientific-document embeddings, research-question similarity, proposal-summary similarity and domain keyword coverage to rank candidate studies for systematic review screening. The pipeline was evaluated on 2,231 Covidence records, including 120 final included studies (prevalence = 5.38%), against keyword-only, TF-IDF, BM25, MiniLM, PubMedBERT and SPECTER-only baselines. SPECTER-hybrid achieved the highest average precision (AP = 0.546), recovered 50% of included studies after screening 4.48% of records, and produced an 11.16-fold enrichment over prevalence. Ablation analysis showed that semantic-keyword combinations consistently outperformed single-signal variants. These findings suggest that citation-informed hybrid ranking can support literature triage while retaining human reviewers as final decision-makers.

Background: Real-world data (RWD) from different countries are increasingly used to support regulatory, health technology assessment (HTA), and population-level evidence generation. However, cross-country analyses are challenged by differences in data provenance, healthcare systems, coding practices, completeness, and clinical workflows. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) is widely used to harmonise heterogeneous RWD sources, but its ability to improve comparability of downstream epidemiological analyses relative to native source data across countries requires empirical evaluation. Methods: We examined RWD from Denmark, Finland and Portugal in their ability to capture epidemiology of female breast cancer (BC) and amyotrophic lateral sclerosis (ALS), exemplifying, respectively, a common disease with established treatment modalities and high survival and a rare fatal disease with scarce treatment options. To enable head-to-head comparison on a semantic level, data were mapped to the OMOP CDM. Data in the native format were used for comparison. In a downstream analysis, we examined disease epidemiology, patient characteristics, treatment, and survival. Results: OMOP conversion enabled a common analytical framework across countries and supported semantically aligned comparisons of key epidemiological and clinical variables. However, cross-country comparability was influenced by differences in data provenance, population coverage, coding practices, availability of clinical details, treatment capture, and healthcare-system-specific workflows. Iterative comparison with native data and external clinical evidence was necessary to identify mapping issues, assess information loss, and ensure high semantic fidelity of the converted data. Overall, OMOP-based estimates were highly consistent with native-data analyses and existing clinical expectations, but residual discrepancies reflected both source-data heterogeneity and decisions in the Extract, Transform, Load (ETL) workflow design. Conclusions: OMOP CDM conversion facilitates semantically meaningful cross-country analyses of RWD by mapping heterogeneous source data to a common structure and standardised vocabularies. However, CDM conversion does not eliminate heterogeneity in the underlying data-generating processes and cannot substitute for study-specific data quality and fitness-for-purpose assessment. Robust use of harmonised RWD for regulatory, HTA, or population-level evidence generation requires iterative benchmarking against native data, clinical expertise, and data-science expertise to support valid interpretation across countries.

Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measures only partially capture the patient experience. We used frontier large language models (LLMs) on patient and caregiver narratives at scale to describe how burden shifts with disease course. Methods. We analyzed 189 testimonials from the Peggy Lillis Foundation corpus, sorted into four cohorts with recurrence (r) and fulminant (f) severity as axes (rfCDI, fCDI, rCDI, non-rfCDI). Two independent LLMs coded eight thematic domains, four fulminant flags, thirteen emerging semantic fields, the dominant dimension, and narrative arcs. Two clinicians independently coded a subset for inter-rater reliability (PABAK, Gwet's AC1). Results. Treatment trajectory was the dominant theme in recurrent disease, whereas death and near-death dominated non-recurrent fulminant narratives. Psychological burden was near-universal in fulminant disease (98.0% in rfCDI, 97.2% in fCDI). Caregiver and bereavement content concentrated in fCDI (66.7%). Diagnostic failure was frequent across recurrent cohorts (47.6 - 56.1%). Bacteriotherapy tracked recurrence (60.2% rfCDI versus 5.6% fCDI). Financial, mental-health, and caregiver burdens were prominent and are currently unaddressed by guidelines. Human-human reliability was substantial (PABAK 0.79 for semantic fields, 0.76 for domains); arc coding was least reliable. Conclusions. Patient narratives reveal a course-dependent, multidimensional burden in CDI. Concrete gaps exist between what patients prioritize, what guidelines recommend, and what therapy access provides. Frontier-LLM coding, validated against clinicians, offers a reproducible route to translate these priorities into research, care, and policy.

Existing evaluations of healthcare AI often treat interoperability as a technical infrastructure issue rather than a factor that directly influences the safety and reliability of clinical AI systems. Yet the quality of Fast Healthcare Interoperability Resources (FHIR) implementation affects whether AI models can operate accurately, fairly, securely, and effectively in real clinical settings. We present FHIRTrustBench, a benchmark for assessing the readiness of FHIR-based clinical AI systems across five complementary dimensions: FHIR implementation quality, AI validation, clinical workflow integration, trustworthiness assessment, and governance readiness. Each dimension is mapped to a distinct category of downstream deployment failure risk. We applied FHIRTrustBench to a corpus of 10 representative sources spanning interoperability standards, implementation studies, electronic health record integration research, healthcare large language model research, and governance frameworks. Each source was scored individually and traceably against the five-dimension rubric. FHIR Specificity achieved the highest dimension mean at 1.3 out of 2.0, while AI Validation received the lowest at 0.3. Even category-leading sources that scored a maximum 2.0 on FHIR Specificity scored 0 on AI Validation. Prospective external validation was reported in no source, and Governance Readiness remained at or below 1.0 across every category. We further identify five interoperability-related AI failure pathways, spanning data integrity, semantic consistency, security, clinical workflow, and generative AI grounding, and propose a deployment lifecycle framework and reporting checklist that translate benchmark scores into deployment-readiness decisions for developers, healthcare organizations, and regulators. FHIRTrustBench provides a practical and reproducible basis for assessing FHIR-enabled clinical AI before deployment and can evolve as interoperability standards and clinical evidence mature.