JAMA

Introduction: Smoking cessation after acute coronary syndrome (ACS) is a Class I recommendation, yet prescription pharmacotherapy use remains low and its real-world cardiovascular effectiveness when added to nicotine replacement therapy (NRT) is poorly characterized. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Network (67 healthcare organizations). Adults hospitalized with ACS who received NRT within one month, serving as a proxy for active smoking status, were identified. Two co-primary propensity-matched (1:1, 50 covariates, caliper 0.10 SD) comparisons evaluated bupropion + NRT and varenicline + NRT individually versus NRT alone; a supportive analysis evaluated combined pharmacotherapy versus NRT alone. All-cause mortality was the primary endpoint. Secondary outcomes included MACE, heart failure exacerbations, major bleeding, TIA/stroke, emergency rehospitalizations, and cardiac rehabilitation utilization, assessed at 6 months and 1 year via Kaplan-Meier analysis. Hazard ratios (HRs) greater than 1.0 indicate higher hazard in the NRT-only group. Results: After matching, the combined analysis comprised 8,574 pairs, the bupropion analysis 4,654 pairs, and the varenicline analysis 2,126 pairs. At 1 year, the combined pharmacotherapy group had significantly lower all-cause mortality (HR 1.26, 95% CI 1.16-1.37), MACE (HR 1.16, 95% CI 1.12-1.21), heart failure exacerbations (HR 1.16, 95% CI 1.08-1.25), major bleeding (HR 1.18, 95% CI 1.08-1.28), and greater cardiac rehabilitation utilization (HR 0.82, 95% CI 0.74-0.92; all p < 0.001). TIA/stroke did not differ significantly. Six-month results were consistent. Both varenicline and bupropion individually showed lower mortality and MACE. A urinary tract infection falsification endpoint showed no between-group differences, supporting matching validity. The pharmacotherapy group had higher rates of new-onset depression, driven predominantly by bupropion recipients. Conclusions: In this propensity-matched real-world analysis, adding prescription smoking cessation pharmacotherapy to NRT after ACS was associated with lower mortality and fewer adverse cardiovascular events, supporting broader integration into post-ACS care pathways.

BackgroundDiabetic kidney disease (DKD) is a leading cause of kidney failure in individuals with type 2 diabetes (T2D), yet risk identification in routine clinical practice remains incomplete. A critical and often overlooked barrier is risk observability: how much of a patients underlying risk is actually captured in their clinical record at the time of screening. Existing prediction models evaluate performance using model-specific thresholds, making it difficult to understand how additional data sources alter real-world screening behavior or which individuals benefit when models are expanded. MethodsWe developed a series of five nested machine learning models evaluated at a one-year landmark following T2D diagnosis using data from the All of Us Research Program (N = 39,431; cases = 16,193). Each successive model added a distinct information layer -- intrinsic risk, laboratory snapshots, medication exposure, longitudinal care trajectories, and social determinants of health (SDOH) -- while retaining all prior features. All models were evaluated under a fixed screening policy targeting 90% specificity, so that the false positive rate remained constant as the information available to the model grew. External validation was conducted in the BioMe Biobank (N = 9,818) without retraining. ResultsDiscrimination improved consistently across layers, from AUROC 0.673 (M1) to 0.797 (M5). Under the fixed screening policy, sensitivity nearly doubled from 0.27 to 0.49, with a cumulative recovery of 30.4% of cases missed by the base model. Gains were driven by distinct subgroups at each transition: laboratory features identified biologically high-risk individuals; medication features captured those with high treatment intensity reflecting advanced cardiometabolic burden; longitudinal care trajectory features rescued cases with biological instability observable only through repeated measurements; and SDOH features recovered individuals with limited clinical observability, with rescue probability highest among those with the fewest recorded monitoring domains. Sparse data in the clinical record indicated low observability, not low risk. Social and genetic features each contributed most when downstream physiologic signal was limited, supporting a contextual rather than universal role for each. In BioMe, discrimination was attenuated (M4 AUROC 0.659), but the relative ordering of information layers was fully preserved, and a systematic upward shift in predicted probability distributions underscored the need for recalibration before deployment in a new setting. ConclusionsDKD risk detection in T2D is substantially improved by integrating complementary information layers under a fixed clinical screening policy, with gains arising from distinct domains that identify at-risk individuals in different clinical contexts. The layered landmark framework introduced here reveals how risk observability -- shaped by monitoring intensity, healthcare engagement, and access -- determines what a screening model can detect, and provides a foundation for context-aware EHR-based screening that accounts for data availability at the time of risk assessment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/26351384v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1cc7f4borg.highwire.dtl.DTLVardef@b92956org.highwire.dtl.DTLVardef@48ffbcorg.highwire.dtl.DTLVardef@8dc627_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Study design and layered DKD screening framework The top row defines the cohort timeline, in which predictors are derived from clinical data collected between T2D diagnosis and the 1-year landmark, and incident DKD is ascertained after the landmark. The second row depicts the nested model architecture, in which five successive models sequentially incorporate intrinsic risk, laboratory snapshot features, medication exposure, longitudinal care trajectories, and social determinants of health, while retaining all features from prior layers. The third row summarizes model development in the All of Us Research Program (N = 39,431) and external validation in the BioMe Biobank (N = 9,818), where the same trained models and risk thresholds were applied without retraining. The bottom row highlights the three evaluation domains: predictive performance, fixed-policy screening, and missed-case recovery context. DKD, diabetic kidney disease; T2D, type 2 diabetes; PRS, polygenic risk scores; AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; PPV, positive predictive value; SHAP, SHapley Additive exPlanations. C_FIG

Objective The decline of the perinatal demise rate is slowing and demises are often unexplained. Significant research has been done regarding diagnostic yield and genetic causes of demise, but little is known about how Geneticist involvement impacts outcomes. The goal of the study was to evaluate post-mortem genetic testing practices and effects of the geneticists involvement. Methods Retrospective data from 111 perinatal demise cases was examined, including rates of prenatal genetic counseling, post-delivery genetics consult, genetic testing, and autopsy investigation. Results In this cohort 54% received genetic testing and 25% received a genetics consult. When compared to those without, cases with genetic specialist involvement were associated with significant increases in testing uptake (p=0.007), diagnostic yield (p<0.001), and patient education (p<0.001). Second trimester stillbirths and those with fewer ultrasound (US) abnormalities were less likely to receive genetic testing (both p values <0.001) and consults (p<0.001, p=0.020). Conclusion Though it was not possible to avoid ascertainment bias, this data demonstrates that geneticist involvement correlates with a higher rate of testing, greater diagnostic yield, and more thorough counseling. These findings underscore the importance of integrating genetics providers into perinatal postmortem healthcare teams.

PurposeDiagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. MethodsWe performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. ResultsThe overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes.. A model without fixed effects attributed [~]10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. ConclusionAcross five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome- and genome-sequencing diagnostic workflows may help reduce inter-site differences.

BackgroundDespite advances in therapy, optimal management of juvenile idiopathic arthritis (JIA) remains challenging. The ability to predict disease progression in JIA can improve personalized treatment decisions, but few reliable clinical predictors have been identified. We developed machine learning approaches to predict disease trajectories in children with JIA. MethodsUsing data from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry (years 2015-2024), we developed machine learning models to predict attainment of inactive disease in children with non-systemic JIA. We applied Dynamic Bayesian Networks (DBN) to model temporal dependencies and causal relationships, and Convolutional Neural Networks (CNN) to capture complex non-linear patterns. Model input included demographic factors, longitudinal clinical factors, and medication use in the preceding 12 months. FindingsA total of 8,093 participants were included. When tested on an independent test cohort, both DBN (AUC:0.76; precision:0.73; recall:0.83; F1-score:0.78; accuracy:0.71) and CNN (AUC:0.76; precision:0.71; recall:0.63; F1-score:0.67; accuracy:0.70) models achieved comparable performance in predicting inactive disease. Disease activity levels in the preceding 12 months, presence of enthesitis and uveitis were the strongest predictors. Causal relationships captured in the DBN model revealed suboptimal care patterns, likely shaped by insurance constraints and a predominantly reactive approach to JIA management. InterpretationOur study demonstrates that machine learning approaches can predict disease trajectories in JIA with good discriminative performance. Unlike prior studies that predict outcomes at single timepoints, our models are the first to predict inactive disease longitudinally. However, suboptimal care patterns in retrospective data limit models capacity to learn treatment-outcome relationships, underscoring critical opportunities to improve JIA care and the need for prospective comparative studies to better inform prediction models. FundingPatient-Centered Outcomes Research Institute (PCORI) Award (ME-2022C2-25573-IC). RESEARCH IN CONTEXT Evidence before this studyNumerous studies have sought to identify clinical predictors of JIA progression and outcomes. However, few reliable predictors have emerged and existing prediction models demonstrate limited performance. As a result, our ability to personalize treatment decisions based on individual risk of severe disease course remains limited. Added value of this studyWe developed novel machine learning models that predict individualized disease trajectories in children with polyarticular and oligoarticular JIA using data from their preceding 12-month clinical course. These models demonstrated strong discriminative performance and outperformed previously published machine learning approaches in JIA. Unlike prior studies limited to single time-point predictions, our models are the first to predict inactive disease longitudinally, enabling a patient-specific projection of disease progression over time. Importantly, our findings also bright to light patterns of suboptimal care, likely driven by insurance constraints and a reactive treatment paradigm, underscoring critical opportunities to improve JIA management. Implications of all the available evidenceOur models have the potential to support clinical decision-making by enabling early identification of children with JIA at risk for unfavorable disease trajectories. In addition, the suboptimal care patterns and systems-level barriers identified through our analyses highlight priority areas for quality improvement initiatives and policy interventions to reduce gaps in JIA care delivery.

BackgroundPaediatric liquid medicines (PLMs) routinely contain sucrose to improve palatability, yet their cariogenic potential is well established. Healthcare professionals awareness and prescribing practices regarding sugar-free PLMs have received limited study in India, particularly in Uttarakhand. MethodsA descriptive cross-sectional study was conducted among 500 healthcare professionals aged [&ge;]25 years, using a pilot-tested structured questionnaire (Cronbachs = 0.85), administered online and in person across Uttarakhand districts (January-March 2024). After excluding 69 incomplete responses, 431 participants were analysed (response rate: 86.2%), comprising general medicine practitioners (49%, n = 211), paediatricians (27%, n = 116), and dental practitioners (24%, n = 104). Descriptive statistics and chi-square tests were applied (p < 0.05). ResultsPrescription decisions were primarily driven by childs age and weight (58%), cost (40%), and pharmaceutical brand (37%). While 88% recognised PLM sweetness and 67% were aware of pH-dental harm links, only 20% associated PLMs with dental caries. Overall awareness of hidden sugars was 73%. Eighty-three percent knew of sugar-free alternatives (50% local availability), yet 80% found them less palatable and 85% costlier. Only 48% routinely provided oral health advice. A statistically significant association was found between specialty and sugar-free PLM awareness (p = 0.03), with dental practitioners recording the highest awareness (90%). ConclusionsHealthcare professionals demonstrated variable levels of knowledge, attitudes, and practices regarding PLMs, with critical gaps in caries recognition (20%) and oral health counselling (48%). Despite high sugar-free PLM awareness, uptake is constrained by perceived cost and palatability barriers. Targeted continuing medical education and policy measures, including sucrose-free labelling promotion, are needed to improve paediatric oral health outcomes in Uttarakhand. KEY MESSAGESO_LIOnly 20% of healthcare professionals in Uttarakhand associated pediatric liquid medicines (PLMs) with dental caries, representing a critical knowledge gap despite 88% recognising their sweetness. C_LIO_LIOverall awareness of hidden sugars in PLMs was 73%, yet only 48% routinely provided post-prescription oral health counsellingsubstantially below international benchmarks. C_LIO_LIEighty-three percent were aware of sugar-free PLM alternatives, but adoption was constrained by perceived inferior palatability (80%) and higher cost ([~]10% premium, cited by 85%). C_LIO_LIDental practitioners demonstrated significantly higher sugar-free PLM awareness than general practitioners and pediatricians (p = 0.03), supporting the case for interprofessional oral health education in medical training. C_LIO_LITargeted continuing medical education (CME) and policy measuresincluding sucrose-free labelling mandates and institutional formulary inclusionare needed to convert awareness into prescribing practice change. C_LI

Background: The 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods: Using electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged 18+ years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results: Analyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6,097/35,765) of cases versus 29.4% (21,449/72,853) of controls were vaccinated. Among adults, 28.2% (21,832/77,477) of cases versus 44.2% (102,560/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED/UC encounters among adults. Conclusions: Influenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease.

BackgroundZero-dose children, defined as those who have not received the first dose of a diphtheria-tetanus-pertussis-containing vaccine (DPT1), are a key indicator of inequitable access to immunization services. Nigeria remains one of the largest contributors to the global burden of zero-dose children. This study estimated the prevalence of zero-dose children aged 12-23 months and identified individual-and community-level determinants using the 2024 Nigeria Demographic Health Survey (NDHS). MethodsA secondary analysis of cross-sectional analysis was conducted using data from 4,711 children aged 12-23 months in the 2024 NDHS kids recode dataset. A multilevel mixed-effects logistic regression model was fitted to account for the hierarchical structure of the data. Four models were compared: null, individual-level, community-level, and combined models. Adjusted odds ratios (AORs) with 95% confidence interval (CI) were used to identify significant determinants at p<0.05. ResultsThe weighted prevalence of zero-dose children was 37.3% (95% CI: 35.1-39.6%). Significant factors included birth order, maternal age, maternal occupation, parental education, household wealth, antenatal attendance, postnatal care utilization, place of delivery, religion, distance to health facilities, and geographical region. Children whose mothers had higher educational attainment, attending antenatal care, deliver in the health facilities, and received postnatal care were significantly less likely to be zero-dose status. Conversely, children from poorer households, those facing distance barriers to health facilities, those belongings to Muslim and traditional religion group and those residing in certain geographical regions had higher odds of zero-dose children, with significant regional variations observed. Conclusionzero-dose vaccination remains highly prevalent in Nigeria and is strongly influenced by socioeconomic disadvantage, maternal healthcare utilization, religion, and regional inequities. Strengthening integrated maternal and child health services and improving access in underserved regions are essential to achieving equitable vaccination coverage.

BackgroundGenetic studies using cardiac magnetic resonance (CMR) imaging have identified loci related to cardiac shape, but most focus on static morphology. The value of a dynamic cardiac shape atlas capturing both shape and function remains unknown. MethodsA dynamic shape atlas comprising CMR-derived shape models at end-diastole and end-systole was combined with genetic and outcome data in 36,992 UK Biobank participants. Dynamic shape principal components (PCs) describing >1% of variance were characterized, and tested for associations with prevalent and incident cardiometabolic diseases, including ischemic heart disease (IHD), heart failure (HF), significant atrioventricular block (AVB), and atrial fibrillation (AF), and independent predictive power alongside standard CMR measures. Genome-wide association studies (GWAS) were performed to identify candidate genes and biological pathways, and polygenic risk scores (PRS) were assessed for disease associations. Mendelian randomization (MR) was performed to test causality of observed disease associations. ResultsWe identified 14 dynamic cardiac shape PCs capturing 83.3% of total dynamic cardiac shape variance. These PCs captured distinct functional remodeling patterns such as variation in annular plane systolic excursion, while remaining only modestly correlated with standard CMR measures. All 14 PCs were associated with at least one incident cardiometabolic disease, with the strongest associations observed for incident IHD, HF, and AVB. Notably, incorporating dynamic shape PCs improved the prediction of incident IHD beyond standard CMR measures. GWAS identified 75 genetic loci associated with dynamic shape, including 14 variants previously unreported for cardiac traits, and candidate genes demonstrated enrichment in pathways related to cardiac development and contractile function. PRS derived from dynamic shape loci were significantly associated with multiple outcomes, most prominently HF. MR identified significant causal relationships between several PCs and cardiometabolic disease. ConclusionsDynamic cardiac shape features capture aspects of cardiac structure and function not fully represented by standard CMR measures. These features are strongly associated with incident cardiometabolic disease and provide new insights into the genetic architecture of cardiac remodeling. Clinical perspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIGenetic and outcome relationships with a dynamic statistical shape model capturing both left and right ventricles at end-diastole and end-systole. C_LIO_LIDemonstration of incremental value over existing cardiac shape models, through capture of functional remodeling not represented by standard imaging measures. C_LIO_LIIdentification of genetic susceptibility loci for dynamic cardiac shape, including 14 variants not previously reported for cardiac traits. C_LI What are the clinical implications?O_LIThe results enhance our understanding of the genetic architecture of dynamic cardiac shape and function in the general population and clarify their relationships with other cardiovascular endophenotypes and incident cardiometabolic diseases. C_LIO_LINewly identified candidate genes expand the biological pathways implicated in cardiac remodeling and provide targets for future functional and mechanistic studies. C_LIO_LIThe improved prediction of incident cardiometabolic disease, particularly ischemic heart disease, achieved by adding dynamic shape PCs to traditional CMR measures suggests potential value for their inclusion in evaluation of patients. C_LI

Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.

Who is affectedIn Japan, approximately 100 million chest radiographs (CXRs) are acquired annually, while only about 7,000 board-certified diagnostic radiologists practice nationwide (Japan Radiological Society workforce statistics; OECD Health Statistics, most recent available year). This implies an average workload exceeding 10,000 imaging studies per radiologist per year if all CXRs were attributed to board-certified diagnostic radiologists (an upper-bound estimate, because in practice many CXRs are primarily read by non-radiologist physicians). In settings such as night shifts, weekends, remote islands, and regional care networks, non-radiologist physicians frequently act as primary readers. Despite strong demand for AI assistance, existing systems are typically limited by one of three shortcomings -- poor cross-institutional generalization, limited interpretability, or inability to generate draft reports -- and consequently see limited clinical deployment. What we builtWe propose a Box-Latent Trinity that embeds each image as a hyperrectangle parameterized by a center c and a radius r, rather than as a single point in a latent space. We further introduce BL-TTA (Box-Latent Test-Time Augmentation), which approximately closes the train-inference gap (exact in the N [-&gt;] {infty} limit; N = 8 suffices in practice) by averaging predictions over samples drawn from within the latent box at inference time. Both components are implemented on top of the frozen MedSAM2 medical imaging foundation model. A single box representation simultaneously supports three functions: (A) theoretically grounded source selection, (B) device-invariant augmentation, and (C) case-based retrieval-augmented generation (RAG). Each prediction is accompanied by retrieved similar prior cases, a calibrated confidence estimate, and clinical-guideline references. How well it performsOn the Open-i CXR corpus (2,954 image-report pairs) under a patient-level 80/10/10 split and 5-seed reproducibility, the full system B5 achieves macro area under the receiver-operating-characteristic curve (macro-AUROC) 0.639 (best-seed test; 5-seed mean 0.626, Table 2; absolute +0.015 over the strongest same-backbone baseline, Merlin-style 0.624), elementwise accuracy 0.753 (absolute +0.072 over Merlin-style 0.681 -- equivalent to approximately 7 fewer label-level errors per 100 (label, image) predictions across 14 finding labels, not per 100 images), and report label-F1 0.435 (absolute +0.086, relative +25 % over the strongest same-backbone report-generation baseline, Bootstrapping-style 0.349). Under simulated pixel-space device-shift intensities up to twice the training distribution, AUROC degrades by only 0.014. Brier score (macro) is 0.061; Cohens{kappa} between two independent rule-based label extractors is 0.702 (substantial agreement); the box radius yields an out-of-distribution (OOD) detection AUROC of 0.595; and the framework provides four structural explainable-AI (XAI) outputs -- retrieved similar cases, confidence tier, per-axis uncertainty, and visual saliency -- which we jointly quantify in a single CXR study, a combination that, to our knowledge, has not been reported previously. O_TBL View this table: org.highwire.dtl.DTLVardef@d8ced6org.highwire.dtl.DTLVardef@1f3471dorg.highwire.dtl.DTLVardef@c1c2f1org.highwire.dtl.DTLVardef@e589bdorg.highwire.dtl.DTLVardef@1b5e410_HPS_FORMAT_FIGEXP M_TBL C_TBL Path to deploymentBecause the complete experiment can be reproduced in under two hours on a consumer-grade GPU (NVIDIA RTX 4060, 8 GB VRAM), the framework can run on compute resources already available at typical healthcare institutions. The approach thus supports the practical delivery of evidence-grounded diagnostic support to night shifts, remote-island care, and secondary readings in health checkups -- settings in which a board-certified radiologist is not locally available. One-sentence summaryReproducible end-to-end in under two hours on a single consumer-grade GPU, the proposed framework outperforms the strongest same-backbone medical-AI baselines on three principal metrics, maintains accuracy under simulated device shifts, and automatically drafts evidence-grounded radiology reports, offering a reproducible and compute-efficient direction toward reducing the reading burden of Japanese radiologists, subject to external validation.

Preconception weight loss by metabolic-bariatric surgery (MBS) improves maternal-fetal outcomes, but little is known about its impact on offspring growth and health. The preconception bariatric surgery and child health outcomes (POSIT) study aims to estimate the effects of maternal MBS-induced preconception weight loss on infant and childhood body size, growth, and related outcomes. This report presents the methods used to construct the POSIT cohort and its baseline characteristics. This retrospective cohort study sampled members from a United States healthcare system aged 18 and older with a singleton, live birth to create three study groups: 1) a treatment group including women who underwent preconception MBS and subsequently became pregnant (n=1,374); 2) a control group matched to the MBS pre-surgery body mass index (BMI) (pre-surgery controls, n=13,740); and 3) a second control group matched to the MBS post-surgical, pre-pregnancy BMI (pre-pregnancy controls, n=13,740). MBS and pre-surgery BMI controls showed slight imbalances in that pre-surgery BMI controls were on average ~6 months younger, had 0.6 lower BMI (44.5 kg/m2) at the time of their pregnancy and were more likely to have become pregnant in earlier years than the MBS group prior to surgery. MBS and pre-pregnancy controls had comparable age (mean {+/-} SD 33 {+/-} 5 years), pre-pregnancy BMI (33 {+/-} 6 kg/m2), and year of delivery. Following matching, the MBS group had similar socioeconomic and health disparities as the pre-surgery control group, and both were worse than pre-pregnancy control group. Pregestational maternal comorbidity index improved after MBS and matched the pre-pregnancy controls. Upon extraction of offspring growth patterns and mediation analyses of maternal weight loss and metabolic responses to MBS, study findings will investigate effects of preconception weight loss by MBS on short- and long-term child health outcomes. Results will guide future studies focusing on improving maternal preconception weight and maternal-fetal outcomes.

Target trial emulation (TTE) offers a principled way to estimate treatment effects using real-world observational data, but analyses of time-varying treatment strategies remain vulnerable to immortal time bias. The clone-censor-weight (CCW) approach is increasingly used to address this problem, yet key aspects of its causal interpretation and implementation remain unclear. In this work, we emulate a target trial using electronic health records (EHRs) to compare completion of a 3-dose 9-valent human papillomavirus vaccination (HPV) series within 12 months versus remaining partially vaccinated among vaccine initiators. We link CCW to the classic potential outcome framework in causal inference, evaluate the role of different weighting mechanisms, and account for within-subject correlation induced by cloning using cluster-robust variance estimation. Our study provides practical guidance for applying CCW in real-world comparative effectiveness studies to address immortal time bias and supports more rigorous and interpretable treatment effect estimation in TTE.

BackgroundThe Toto Bora trial tested whether a course of azithromycin reduced rates of re-hospitalization or death in the 6 months following hospitalization among Kenyan children. We hypothesized that azithromycin would reduce enteric bacteria and increase carriage of macrolide resistance in the subsequent 3 months. MethodsKenyan children (1-59 months) hospitalized and subsequently discharged for non-traumatic conditions provided fecal samples before and 3 months after randomization to a 5-day course of azithromycin or placebo. Quantitative PCR identified enteropathogens and AMR-conferring genes in fecal samples. Generalized estimating equations assessed the impact of the randomization arm on pathogen and resistance gene detection, accounting for baseline presence and site. ResultsAmong 1,393 baseline stools, 12.4% had at least one bacterial enteropathogen, 94.7% had at least one macrolide-resistance gene, and 92.6% had at least one beta-lactamase-resistance gene identified. At month 3, children randomized to azithromycin had a 6.1% higher likelihood of carrying a macrolide resistance gene compared to placebo (adjusted prevalence ratio [aPR], 1.06; 95% CI, 1.04-1.08; P<0.001). Specifically, azithromycin randomization was associated with a higher relative prevalence of erm(B) (aPR, 1.09 [95% CI, 1.04-1.15]; P=0.001), erm(C) (aPR, 1.23 [95% CI, 1.14-1.31]; P<0.001), msr(A) (aPR, 1.14 [95% CI, 1.04-1.25]; P=0.007), and msr(D) (aPR, 1.07 [95% CI, 1.03-1.11]; P=0.001). There was no difference in overall bacterial pathogen prevalence (18.9% vs 17.3%) between randomization arms, but a slightly lower proportion of children had Shigella after randomization in the azithromycin arm (3% vs. 5%, aPR, 0.79 [95% CI, 0.62, 1.01]; P=0.063). InterpretationAzithromycin at hospital discharge was associated with higher carriage of macrolide-resistance-conferring genes in the post-discharge period compared with placebo, without significant declines in enteric pathogen carriage other than modest changes to Shigella. The potential benefits and risks of empiric azithromycin need to be considered, as children are increasingly exposed to this broad-spectrum antibiotic.

BackgroundRecruitment remains a major barrier to timely clinical trial completion. Trialshub is an LLM-powered, chat-based platform intended to help users identify relevant trials and connect with coordinators to streamline recruitment workflows. ObjectiveTo evaluate the perceived usability and operational value of Trialshub, and identify implementation considerations for real-world deployment. MethodsA usability test was conducted at Morehouse School of Medicine for the Trialshub application. Purposively selected participants included clinical research coordinators and individuals with and without clinical trial search experience. Participants completed a pre-test survey assessing demographics, digital health information behaviors, and familiarity with AI tools, followed by a moderated usability session using a Trialshub prototype. Users completed scenario-based tasks (locating a breast cancer trial, reviewing results, and initiating coordinator contact) using a think-aloud protocol. Task ratings, screen recordings, and transcribed feedback were analyzed descriptively and thematically, and reported. ResultsParticipants reported high comfort with using digital tools and moderate-to-high familiarity with AI. Trialshubs chat-first design, guided prompts, and checklist-style eligibility display were perceived as intuitive and reduced cognitive load. Fast access to trials and the coordinator-contact workflow were viewed positively. Key usability issues included uncertainty at step transitions, insufficient cues for selecting results and next actions, and inconsistent system reliability (loading delays, errors, and broken trial detail pages). Participants also noted redundant questioning due to limited conversational memory, requested improved filtering/sorting, and clearer calls-to-action. All participants indicated that Trialshub has strong potential to meaningfully improve clinical trial processes. ConclusionsTrialshub shows promise for improving trial discovery and recruitment workflows, with identified design implications for real-world deployment.

Introduction Secondary use of electronic health records (EHRs) often requires transforming raw clinical information into research-grade data. A central step in this process is EHR phenotyping - the identification of patient cohorts defined by specific medical conditions. Although numerous approaches exist, from ICD-based heuristics to supervised learning and large language models (LLMs), the field lacks standardized benchmark datasets, limiting reproducibility and hindering fair comparison across methods. Methods We developed the MIMIC-IV Phenotype Atlas (MIPA) dataset, an adaptation of MIMIC-IV that provides expert-annotated discharge summaries across 16 phenotypes of varying prevalence and complexity. Two independent clinicians reviewed and labeled the discharge summaries, resolving disagreements by consensus. In parallel, we implemented a processing pipeline that extracts multimodal EHR features and generates training, validation, and testing datasets for supervised phenotyping. To illustrate MIPA's utility, we benchmarked four phenotyping methods : ICD-based classifiers, keyword-driven Term Frequency-Inverse Document Frequency (TF-IDF) classifiers, supervised machine learning (ML) models, and LLMs on the task. Results The final MIPA corpus consists of 1,388 expert-annotated discharge summaries. Annotation reliability was high (mean document-level kappa = 0.805, mean label-level kappa = 0.771), with 91% of disagreements resolved through consensus review. MIPA provides high-quality phenotype labels paired with structured EHR features and predefined train/validation/test splits for each phenotype. In the benchmarking case study, LLMs achieved the highest F1 scores in 13 of 16 phenotypes, particularly for conditions requiring contextual interpretation of clinical narrative, while supervised ML offered moderate improvements over rule-based baselines. Conclusion MIPA is the first publicly available benchmark dataset dedicated to EHR phenotyping, combining expert-curated annotations, broad phenotype coverage, and a reproducible processing pipeline. By enabling standardized comparison across ICD-based heuristics, ML models, and LLMs, MIPA provides a durable reference resource to advance methodological development in automated phenotyping.

BackgroundPolygenic embryo screening (PES) has recently become available to in-vitro fertilization (IVF) patients, allowing them to evaluate the genetic risk of each of their embryos for polygenic conditions such as heart attack or diabetes. Initial modeling predicted that transferring the embryo with the lowest genetic risk for one or more diseases would substantially reduce prevalence in the next generation, with relative risk reductions up to 50%. However, these models assumed the availability of a prespecified number of embryos and that the embryo with the most favorable polygenic risk is born once transferred to the uterus. In reality, a large percentage of embryo transfers do not lead to live births, and IVF frequently results in no or only a single live birth. MethodsTo quantify the expected risk reduction in the context of IVF, we used two datasets: 6944 ovarian stimulation cycles from 4452 Italian infertility patients and 2138 stimulation cycles of egg donors. In both datasets, we simulated the hypothetical application of PES in these cycles by assigning patients and their embryos randomly drawn polygenic risk scores for a given disease, assuming that embryos have been transferred in increasing order of their risk, and tracing their birth outcomes. We then compared the risk of the embryo born after hypothetical PES to the risk of an embryo born without PES. We either considered only completed cycles or integrated over possible birth outcomes of non-transferred embryos in incomplete cycles. ResultsIn stimulation cycles in infertility patients in which all embryos have been transferred and at least one child was born, we estimate that PES will result in relative risk reductions of just {approx}1-3%. In an intention-to-screen analysis of all completed cycles (regardless of birth outcomes), relative risk reductions are under 0.5%. The risk reductions increase, as expected, with more euploid blastocysts and with younger maternal age. Including incomplete cycles (in which not all embryos have been transferred) increases risk reductions to {approx}2-5%, due to the availability of more euploid blastocysts and a higher live birth rate per transfer in these cycles. Pooling all embryos from all cycles of the same patient increases risk reductions to {approx}5-10%. Relative risk reductions in egg donor cycles reach {approx}20% even with a single stimulation cycle per donor. ConclusionsWith the exception of particularly good-prognosis patients or cycles, typical infertility patients would benefit little from PES. In fertile patients, as represented by egg donors, PES is predicted to achieve greater relative risk reductions. However, even though these reductions are still substantially lower than prior estimates that did not account for realistic live birth rates. Ethical, social, and clinical issues associated with offering PES in the general population should be prioritized in future research.

STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSSexual dysfunction can occur after midurethral sling (MUS) and transvaginal prolapse surgery. It remains unclear whether these procedures impact the clitoris, despite its role in sexual function and proximity to the MUS and vagina. ObjectivesTo compare postoperative sexual function and clitoral features by MUS and vaginal surgery approach after transvaginal prolapse repair with/without concomitant MUS. DesignCross-sectional ancillary study of magnetic resonance imaging (MRI) and sexual function data from the Defining Mechanisms of Anterior Vaginal Wall Descent study. SettingEight clinical sites in the US Pelvic Floor Disorders Network. Participants: 88 women with uterovaginal prolapse who underwent vaginal mesh hysteropexy or vaginal hysterectomy with uterosacral ligament suspension with/without MUS between 2013-2015. Data were analyzed between September 2021-June 2023. ExposuresBetween June 2014-May 2018, participants underwent pelvic MRI 30-42 months after surgery, or earlier if reoperation was desired. Sexual activity and function at baseline and 24-48-month follow-up were evaluated using the Pelvic Organ Prolapse/Incontinence Sexual Questionnaire, IUGA-Revised (PISQ-IR). Clitoral features were obtained from postoperative MRI-based 3-dimensional models. Main Outcomes and MeasuresPISQ-IR scores and clitoral features (size, position). ResultsEighty-two women (median [range] age, 65 [47-79] years) were analyzed: 45 MUS (22 hysteropexy, 23 hysterectomy) and 37 No-MUS (19 hysteropexy, 18 hysterectomy). Postoperatively, 25 MUS, 12 No-MUS, 20 hysteropexy, and 17 hysterectomy patients were sexually active (SA). Overall, within the MUS and vaginal surgery groups, sexual function remained unchanged or improved (most PISQ-IR change from baseline scores were [&ge;]0) among SA and NSA women. Among SA women after surgery, the MUS group (vs No-MUS) had a poorer PISQ-IR arousal/orgasm (SA-AO) score (median, 3.5 vs 4.3; P=.02). The hysteropexy group (vs hysterectomy) had less improvement in PISQ-IR SA-AO score (median, 0.0 vs 0.3; P=.01). Women with MUS (vs without) had a smaller clitoral glans thickness (median, 9.0 mm vs 10.0 mm; P=.008) and clitoral body volume (median, 2783.5 mm3 vs 3587.4 mm3; P=.01). Conclusions and RelevanceSA women with MUS (vs without) or hysteropexy (vs hysterectomy) experienced poorer postoperative sexual function. MUS was linked to a smaller clitoris. Future studies should explore surgery-induced changes in clitoral anatomy and sexual function. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow do sexual function and clitoral anatomy differ by midurethral sling placement and vaginal surgery approach? FindingsThis cross-sectional study compared patient-reported sexual function outcomes and 30-42-month postoperative magnetic resonance imaging-based 3-dimensional clitoral models of 82 women after vaginal prolapse surgery with or without concomitant midurethral sling. Midurethral sling (vs no sling) and vaginal mesh hysteropexy (vs vaginal hysterectomy) were associated with poorer postoperative sexual function outcomes. Additionally, midurethral sling was associated with a smaller clitoral glans and body. MeaningMidurethral sling and vaginal mesh hysteropexy were associated with, and may adversely alter, postoperative sexual function and/or clitoral anatomy. VISUAL ABSTRACT/PROMOTIONAL IMAGE O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26351291v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@904497org.highwire.dtl.DTLVardef@187514aorg.highwire.dtl.DTLVardef@e9e799org.highwire.dtl.DTLVardef@640f1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectivesTo evaluate the effect of surgical hubs on the volume of surgeries, patient waiting times, and length of hospital stay for elective hip and knee replacements in the English NHS. DesignA retrospective longitudinal study using a difference-in-differences approach to compare changes in outcomes at NHS trusts that opened surgical hubs with those that did not. SettingThe study was set in the English NHS, using administrative data from NHS acute trusts providing elective hip and knee replacements between April 2014 and September 2024. ParticipantsThe study included 76 NHS trusts. The treatment group consisted of 29 trusts that opened a surgical hub for trauma and orthopaedic surgery during the study period. The control group consisted of 47 trusts that did not. 48 trusts that performed fewer than 1,000 relevant procedures over the ten-year period or that reported data for fewer than 41 of the 42 quarters in the sample period were excluded. InterventionThe phased introduction of surgical hubs dedicated to elective procedures at 29 NHS trusts between Q1 2020 and Q3 2024. Main outcome measuresThe three main outcomes were, measured at the trust-quarter level: the total number of elective primary hip and knee replacements (surgical volume), the average length of stay in hospital, and the average waiting time from being added to the waiting list to hospital admission. ResultsThe opening of a surgical hub was associated with an increase of 43.75 hip and knee replacement surgeries per quarter (95% CI: 22.22 to 65.28), which represents a 19.1% increase compared to the pre-hub mean. Length of stay was reduced by 0.32 days (95% CI: - 0.48 to -0.16), a 7.8% reduction. There was no statistically significant effect on average waiting times (-14.96 days, 95% CI: -33.11 to 3.19). ConclusionsSurgical hubs appear to be effective at increasing the number of hip and knee replacements and reducing the time patients spend in hospital. However, in this study, they did not lead to a statistically significant reduction in waiting times overall.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.