P404 Underrepresentation of minorities and lack of race reporting in ulcerative colitis drug development clinical trials
Bibliographic record
Abstract
Abstract Background Historically, inflammatory bowel disease (IBD) trials report high enrollment rates for white-caucasian patients. To promote initiatives towards diversifying patients enrolled in clinical trials, we assessed the reporting of race/ethnicity of patients enrolled in pharmaceutical clinical trials for ulcerative colitis (UC). Methods A previous systematic review of all placebo-controlled trials in adult patients with UC examining different therapeutic drug classes was performed from inception to December 2020. Trial and patient characteristics were summarized according to the type of variable. Categorical variables were summarized by displaying the number and percentage of trials or participants for each category. Continuous variables were summarized by displaying the weighted mean, weighted standard deviation, and range. Means and standard deviations (SD) were weighted by the number of participants in each trial. Results Descriptive statistics of trial and patient characteristics for, 95 induction trials and, 29 maintenance trials are summarized in Table, 1. Race was reported in, 37.9% (36/95) of induction studies, with most participants being White (86.3%;, 8610/9976). Furthermore, 21/36 (58.3%) studies reported race as White vs. non-White participants; and, 15/36 (41.7%) studies provided further breakdown, including Black (115/3414 patients [3.4%]), Asian (448/4136 patients [10.8%]), and “Other” (127/4136 patients [3.1%]). Moreover, 3/36 studies misreported race as ethnicity and, only, 3/36 studies reported ethnicities correctly, classifying patients as Hispanic/Latinx vs. Non-Hispanic/Latinx. For maintenance trials, 34.5% (10/29) studies reported race, with majority of participants being White (85.6%;, 2778/3246). Additionally, 6/10 studies reported only a White vs. non-White participants comparison, while, 4/10 reported other races, including Black (45/1145 patients [3.9%]), Asian (40/888 patients [4.5%]), and “Other” (26/1145 patients [2.3%]). No studies reported ethnicity. Conclusion Given the increasing burden of IBD in developing countries, differences amongst racial and ethnic groups are important to understand since they can influence disease phenotype, response to therapy, and safety outcomes.1 We found poor race reporting in UC clinical trials and observed that the vast majority of participants enrolled were White. These findings suggest that the population prevalence of UC amongst different racial groups is not reflected in clinical trial populations, being important to raise awareness of the existing barriers that prevent patients from accessing clinical trials. Reporting of race and ethnicity in UC clinical trials should be mandatory, and a requirement to enroll a certain percentage of non-White patients should be considered.2
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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 | 0.266 | 0.471 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".