Racial and Ethnic Composition of Cancer Clinical Drug Trials: How Diverse Are We?
Bibliographic record
Abstract
Many approved drugs demonstrate different pharmacokinetics, pharmacodynamics, and/or safety across racial and ethnic groups. The primary objective of the current study was to summarize the racial and ethnic makeup of cancer clinical drug trials using cancer drugs approved by the U.S. Food and Drug Administration (FDA) between January 1, 2010, and July 31, 2016. In clinical studies used for FDA approvals, 82.3% of participants identified as white, 10.2% as Asian, 2.3% as black, and 4.7% as Hispanic. Black participants made up 7.7% of U.S. and Canadian cancer clinical drug trials and 2.6% of global cancer clinical drug trials while Asian participants made up 13.5% of global cancer clinical drug trials but only 1.8% of U.S. and Canadian cancer clinical drug trials. The current study indicates that although cancer clinical drug trials have become more inclusive of Asian participants, other racial and ethnic minority groups remain under-represented. This may result in an inadequate understanding of drug safety and efficacy in many racial and ethnic populations.
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 | 0.180 | 0.422 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| 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".