Racial and ethnic diversity in global neuroscience clinical trials
Bibliographic record
Abstract
Background: Despite efforts to increase diversity in neuroscience trials, racial and ethnic minority groups remain underrepresented. Disparities in clinical trial participation could reflect unequal opportunities to participate and may contribute to decreased generalizability of findings and failure to identify important differences in efficacy and safety outcomes. Methods: We retrospectively reviewed the F. Hoffmann-La Roche database for global, multicenter, neuroscience clinical trials from February 2016 to February 2021 and summarized and stratified race and ethnicity distributions by clinical trial therapeutic area and by country. These data were then compared to national population data for each study's targeted age group (available for studies conducted in the US, Canada, and the UK). The underrepresentation or overrepresentation of each racial and ethnic group was summarized. Results: The analysis population included 8015 participants from 47 countries. Globally, 85.6 % of participants were White, 7.1 % were Asian, 1.6 % were Black, 1.3 % were American Indian or Alaska Native, less than 0.1 % were Native Hawaiian or other Pacific Islander, 0.7 % were of multiple races, and 3.6 % were of other/unknown race. White individuals predominated in all but one trial. Black individuals were underrepresented in all trials but one. Asian individuals were overrepresented in approximately 20 % of trials. In the US, 7.3 % of participants were of Hispanic or Latino ethnicity vs 16.4 % of the US population. Conclusion: The findings and learnings from this summary and analysis demonstrate the need for continued awareness and new approaches in designing studies that reflect population diversity.
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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 | 0.115 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".