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Background Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. However, most adaptive designs rely on predefined subgroups and are limited in their ability to uncover unknown or complex sources of HTE. Bayesian statistical methods offer a flexible alternative, enabling real-time learning and adaptation within trials. This review evaluates Bayesian methods used to detect hidden HTE in adaptive clinical trials, with attention to their methodological innovations, operating characteristics, and consideration of equity and inclusion in trial design. Methods We conducted a systematic search of MEDLINE, Embase, and other databases to identify original studies that developed Bayesian methods for detecting unknown HTE within adaptive clinical trial designs. Eligible studies were reviewed and synthesized based on design features, statistical methodology, operating characteristics, reproducibility, and whether equity-related factors were explicitly considered. Equity considerations included whether studies incorporated variables related to underrepresented populations—such as age, sex, race/ethnicity, or geography—examined intersectional subgroup effects, or explicitly framed their methods as tools to address health disparities. Results Of 2826 screened records, seven studies met inclusion criteria. Bayesian methods included random partition models, spatial models, logistic regression with dimension reduction, adaptive randomization using machine learning classifiers, and adaptive enrichment or platform designs incorporating model averaging or latent subgroup estimation. In simulation studies, these methods often showed improvements in subgroup detection, efficiency, or power relative to non-Bayesian comparators. None were tested using real-world trial data. Reproducibility was limited overall, with analytic code only available for the three most recent studies. Notably, none explicitly framed their methods as tools to address inequities in treatment outcomes across population subgroups. Conclusions The small number of simulation-based studies illustrates preliminary but promising directions for applying Bayesian methods to detect HTE in adaptive clinical trials. While these approaches demonstrate potential to enhance trial adaptability, scalability, and inclusiveness, current evidence remains limited and largely conceptual. Incorporating an equity lens into future methodological development, alongside greater emphasis on empirical validation and open science practices, will be essential to determine their practical value in advancing equitable clinical research.

It's time for more discussion about how best to incorporate chaperones in primary care.

Renal cell carcinoma (RCC) accounts for approximately 80% of all primary renal neoplasms in United States causing approximately 65 000 new cases of RCC and 14 000 deaths each year. Symptoms of RCC typically include weight loss and night sweats but may also feature paraneoplastic phenomena in advanced stages as well as flank pain, gross hematuria, scrotal varicocele, inferior vena cava pathology, and a palpable abdominal mass. In this article, we present the course of a patient with advanced RCC, from initial presentation through workup and to eventual diagnosis. The case features late-onset symptoms, extensive paraneoplastic phenomena, and significant physical examination findings. We also review the literature available on RCC and critically analyze inefficiencies of the workup retrospectively.

The presence of cardiovascular abnormalities in patients with spontaneous cerebrospinal fluid (CSF) leaks are not well-documented in the literature, as cardiovascular evaluation is not generally pursued if a patient does not exhibit additional clinical features suggesting an inherited connective tissue disorder. We aimed to assess this association, enrolling a consecutive group of 50 patients referred for spinal CSF leak consultation. Through echocardiographic evaluation and detailed medical history, we estimate that up to 20 % of patients presenting with a spontaneous CSF leak may have some type of cardiovascular abnormality. Further, the increase in prevalence of aortic dilatation in our cohort was statistically significant in comparison to the estimated population prevalence. This supports a clinical basis for echocardiographic screening of these individuals for cardiovascular manifestations that may have otherwise gone unnoticed or evolved into a more severe manifestation.