Race and ethnicity reporting and representation in hemophilia clinical trials.

ABSTRACT: Racial and ethnic representativeness in clinical trials is crucial to mitigate disparities in outcomes; however, diversity among hemophilia trials is unknown. The aim of this study is to examine the reporting and representation of race and ethnicity in trials of people with hemophilia (PwH). In this cross-sectional study, the ClinicalTrials.gov database was queried in April 2023 for interventional clinical trials involving PwH between 2007 and 2022. The distribution of participants (observed) was compared with expected proportions based on US Hemophilia Treatment Center (HTC) and country-specific census data with observed-to-expected ratios (OERs). Of 129 trials included, 94.6% were industry sponsored, with a mean of 62 participants and mean age of 26.8 years. Overall, 52.0% (n = 66) of trials reported data on race and ethnicity, increasing from 13.9% in 2007-2012 to 22.5% in 2013-2016 to 100% in 2017-2022 (P = .001). Among these 66 trials, 65.8%, 22.8%, 5.1%, 3.9% of participants were White, Asian, Hispanic, and Black, respectively. OERs were 10% to 20% lower for White participants vs US HTC, and US, UK, and Canadian census populations and ∼75% lower for Black or Hispanic participants when compared with US HTC and US census population. OERs for Asian participants were 1.6 to 3 times higher than Canada, US, and UK census populations. The reporting of race and ethnicity in hemophilia trials has drastically improved; however, Black and Hispanic PwH remain especially underrepresented. To address these disparities, stakeholders across the clinical trial enterprise need to implement strategies to ensure equitable participation.

Physician Assessment of ChatGPT and Bing Answers to American Cancer Society's Questions to Ask About Your Cancer.

OBJECTIVES: Artificial intelligence (AI) chatbots are a new, publicly available tool for patients to access health care-related information with unknown reliability related to cancer-related questions. This study assesses the quality of responses to common questions for patients with cancer. METHODS: From February to March 2023, we queried chat generative pretrained transformer (ChatGPT) from OpenAI and Bing AI from Microsoft questions from the American Cancer Society's recommended "Questions to Ask About Your Cancer" customized for all stages of breast, colon, lung, and prostate cancer. Questions were, in addition, grouped by type (prognosis, treatment, or miscellaneous). The quality of AI chatbot responses was assessed by an expert panel using the validated DISCERN criteria. RESULTS: Of the 117 questions presented to ChatGPT and Bing, the average score for all questions were 3.9 and 3.2, respectively ( P < 0.001) and the overall DISCERN scores were 4.1 and 4.4, respectively. By disease site, the average score for ChatGPT and Bing, respectively, were 3.9 and 3.6 for prostate cancer ( P = 0.02), 3.7 and 3.3 for lung cancer ( P < 0.001), 4.1 and 2.9 for breast cancer ( P < 0.001), and 3.8 and 3.0 for colorectal cancer ( P < 0.001). By type of question, the average score for ChatGPT and Bing, respectively, were 3.6 and 3.4 for prognostic questions ( P = 0.12), 3.9 and 3.1 for treatment questions ( P < 0.001), and 4.2 and 3.3 for miscellaneous questions ( P = 0.001). For 3 responses (3%) by ChatGPT and 18 responses (15%) by Bing, at least one panelist rated them as having serious or extensive shortcomings. CONCLUSIONS: AI chatbots provide multiple opportunities for innovating health care. This analysis suggests a critical need, particularly around cancer prognostication, for continual refinement to limit misleading counseling, confusion, and emotional distress to patients and families.