S1944 Recruitment of Minority Patients in Metabolic Dysfunction-Associated Steatotic Liver Disease Clinical Trials
Bibliographic record
Abstract
Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD) and steatohepatitis (MASH) are the second leading indications for liver transplant in the United States. The prevalence is unevenly distributed across ethnic groups with 22.3% of Hispanic patients disproportionately affected in the United States. This study aimed to assess racial and ethnic diversity among MASLD clinical trials. Methods: We performed a systematic review of randomized controlled trials (RCTs) of MASLD/MASH therapies in the US and Canada from 2005-2024. Multinational RCTs involving the US were included. Data including patient age, sex, race/ethnicity, and therapeutic intervention type were collected. Meta-analysis was used to determine the pooled prevalence of different racial and ethnic groups. Descriptive statistics were used to compare racial and ethnic trial inclusion to previously reported MASLD prevalences in the US. Results: Ninety-one RCTs met inclusion criteria. The studied therapeutics included medications (n=84), lifestyle interventions (n=6), and fecal microbiome transplant (n=1). When reported, the median age of study participants was 52.6 years (24.1 - 69.7) with 58% female subjects. Seventy-8 studies (85.7%) reported racial data and fifty-6 (61.5%) included ethnicity data. Among the 16,111 patients enrolled in these trials, 3,298 (20.5%) were of Hispanic ethnicity. Meta-analysis revealed a pooled prevalence of 78.8% in White patients (95% CI 72.9 – 84.6, I2 = 97%), 6.2% in Asian patients (95% CI 4.15 – 8.31, I2 = 94%), and 2.5% in Black patients (85% CI 1.58 – 3.44, I2 = 70%). Pooled prevalence of Hispanic patients was 31.7% (95% CI 26.8 - 36.6, I2 = 94.6%). This prevalence was higher than the reported national Hispanic prevalence of 22.3%, which may be partially explained by the substantial heterogeneity in the analysis. Hispanic enrollment also increased over time from 20% (2009 – 2014), 31% (2015 – 2019), to 35% (2020 – 2024). Conclusion: While the majority of studies reported data on participant race, only 61.5% included ethnicity. Since MASLD disproportionately affects Hispanic patients, it is imperative that clinical trials make a targeted effort to diversify patient recruitment. Compared to previous efforts, this study shows that trials are increasingly including Hispanic patients, yet need to increase inclusion of ethnicity data (see Figure 1).Figure 1.: Pooled prevalence of Hispanic patients among studies that reported ethnicity data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".