Reporting and enrollment disparities in hematologic malignancy trials between 2000–2023
Bibliographic record
Abstract
Despite initiatives to enhance diversity in clinical trials (CTs), disparities persist in hematologic malignancy (HM) studies. We reviewed 1,230 US-based phase II-III HM CTs (2000-2023) including 149,434 participants and compared enrollment to SEER benchmarks. Race was reported in 59% of trials and ethnicity in 40%, with significant improvement over time. Trials initiated in 2016 or later were more likely to report race (OR 36.3) and ethnicity (OR 8.0) than those before 2008. Compared with NIH-sponsored studies, institutional (OR 0.34) and industry trials (OR 0.52) had lower odds of reporting demographics. Black and Hispanic individuals were consistently underrepresented, most notably in multiple myeloma (7.0% vs. 20.0% expected) and acute lymphoblastic leukemia (21.5% vs. 36.9% expected). NIH-funded trials enrolled more Black participants than other sponsor types. These findings emphasize the need for enforceable mandates on race and ethnicity reporting, as well as representation targets, to ensure equitable access and generalizability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 | 0.214 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".