The roadmap to integrate diversity, equity, and inclusion in hematology clinical trials: an American Society of Hematology initiative
Bibliographic record
Abstract
ABSTRACT: Clinical trial design for classical hematologic diseases is difficult because samples sizes are often small and not representative of the disease population. The American Society of Hematology initiated a roadmap project to identify barriers and make progress to integrate diversity, equity, and inclusion into trial design and conduct. Focus groups of international experts from across the clinical trial ecosystem were conducted. Eight issues identified include (1) harmonization of demographic terminology; (2) engagement of lived experience experts across the entire study timeline; (3) awareness of how implicit biases impede patient enrollment; (4) the need for institutional review boards to uphold the justice principle of clinical trial enrollment; (5) broadening of eligibility criteria; (6) decentralized trial design; (7) improving access to clinical trial information; and (8) increased community physician involvement. By addressing these issues, the hematology community can promote accessible and inclusive trials that will further inform research, clinical decision-making, and care for patients.
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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 | 0.525 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.008 | 0.039 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".