Race and age disparities in randomized trials of acute myeloid leukemia: a systematic review and meta-analysis
Bibliographic record
Abstract
There are significant racial and ethnic disparities in the incidence and survival of patients with acute myeloid leukemia (AML). Understanding the discrepancies in enrollment in randomized controlled trials (RCTs) is important for better informing access to care and clinical trial conduct. We systematically reviewed the literature on the enrollment of racial/ethnic minorities and older adults into RCTs of AML. MEDLINE was searched from inception through June 2023 for RCTs on disease-modifying therapy for AML in adults. The proportion of trials reporting racial and ethnic subgroups, the enrollment proportions for each race, and age ≥65 years were determined, which were stratified by year, trial phase, and geographic location. A meta-analysis of enrollment incidence ratios (EIRs), the ratio of trial proportions of members of a racial and ethnic subgroup divided by the US population-based incidence, was conducted. A total of 7759 titles and abstracts and 157 full texts were screened, yielding 90 studies. Up to 23.3% of trials reported race or ethnicity, and 28.9% reported age ≥65 years. Of the trials reporting race, 4.7% of participants were Black, 9.8% Asian/Pacific Islander, 0.5% Native American/Alaska Native, 80.8% White, and 3.4% Hispanic. Hispanic patients (EIR, 0.28), and Asian patients (EIR, 0.16) were significantly underrepresented, whereas White patients (EIR, 1.23) were significantly overrepresented. When stratifying by year, we found an increase in the proportion of trials reporting on race in the last 10 years (46.2% vs 19.5%) and an increase in the last 20 years in the proportion of racial minorities enrolled.
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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".