Twenty-year trends in racial and ethnic enrollment in large diabetes randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Lack of representativeness in Black, Indigenous, and People of Colour (BIPOC) enrollment could compromise the generalizability of study results and health equity. This study aimed to examine trends in BIPOC groups enrollment in diabetes randomized controlled trials (RCTs) and to explore the association between trial factors and high-enrollment of BIPOC groups. METHODS: We systematically searched the literature on large diabetes RCTs with a sample size of ≥ 400 participants published between 2000 and 2020. We assessed temporal trends in enrollment of racial and ethnic groups in the included trials. Logistic and linear regression analyses were used to explore the relationship between trial factors and the high-enrollment defined by median enrollment rate. RESULTS: A total of 405 RCTs were included for analyses. The median enrollment rate of BIPOC groups was 24.0%, with 6.4% for the Black group, 11.2% for Hispanic, 8.5% for Asian, and 3.0% for other BIPOC groups respectively. Over the past 20 years, the BIPOC enrollment showed an increased trend in the diabetes RCTs, ranging from 20.1 to 28.4% (P for trend = 0.041). A significant trend towards increased enrollment for Asian group was observed. We found that weekly or daily intervention frequency (OR = 0.48, 95% CI: 0.26, 0.91) and duration of intervention > 6.5 month (OR = 0.59, 95% CI: 0.37, 0.95) were significantly related to decreased odds of high-enrollment, while type 2 diabetes (OR = 1.44, 95% CI: 1.04, 1.99) was associated with high-enrollment of BIPOC groups. CONCLUSIONS: The enrollment of BIPOC was found to increase in large diabetes RCTs over the past two decades; some trial factors may be significantly associated with BIPOC enrollment. These findings may highlight the importance of enrollment of BIPOC groups and provide insights into the design and implementation of future clinical trials in diabetes.
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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 | 0.308 | 0.481 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".