Racial and ethnic subgroup reporting in diabetes randomized controlled trials published from 2000 to 2020: A survey
Bibliographic record
Abstract
BACKGROUND: It remained unknown about the status of and trends in racial/ethnic subgroup reporting in the diabetes trials over the past two decades. OBJECTIVES: In this survey, we aimed to evaluate the current state of and temporal trends in subgroup reporting by race/ethnicity regarding the effects of interventions in diabetes randomized controlled trials (RCTs) from year 2000-2020 and to explore the potential trial factors in relation to racial/ethnic subgroup reporting. METHODS: We searched electronic databases for eligible diabetes RCTs. The outcome was whether the trials had the event of racial/ethnic subgroup reporting regarding the intervention effects on trial primary outcomes. Poisson regression was used to assess the temporal trends in racial/ethnic subgroup reporting, and univariable logistic regression models were employed for evaluating trial factors related to racial/ethnic subgroup reporting. RESULTS: A total of 405 diabetes RCTs were eligible for inclusion. There were 26 (6.42%) trials with racial/ethnic subgroup reporting. A chronological trend towards increased rates of racial/ethnic subgroup reporting was observed; however, the trend was not statistically significant (p = 0.07). Advanced patients' age (Odds ratio [OR] = 2.92, 95% confidence interval [CI]: 1.24-6.88), follow-up duration (OR = 3.53, 95% CI: 1.13-11.00), and BIPOC (Black, Indigenous, and People of Colour) enrolment (OR = 2.39, 95% CI: 1.01-5.62) were found to positively relate with racial/ethnic subgroup reporting, while the industrial funding was associated with decreased reporting (OR = 0.43, 95% CI: 0.19-0.97). Less than one fourth of the trials with racial/ethnic subgroup reporting predefined the subgroup analysis. CONCLUSIONS: The majority of diabetes RCTs did not report intervention effects by racial/ethnic subgroup, which was not temporally improved over the past two decades. More efforts and strategies are needed to improve the racial/ethnic subgroup consideration and reporting in diabetes trials.
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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 | 0.165 | 0.453 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".