Under‐representation of racial minorities in prostate cancer studies submitted to the US Food and Drug Administration to support potential marketing approval, 1993‐2013
Bibliographic record
Abstract
BACKGROUND: US Food and Drug Administration (FDA) approval of new drugs depends on results from clinical trials that must be generalized to the US population. However, racial minorities are frequently under-represented in clinical studies. The enrollment of racial minorities was compared in key clinical studies submitted to the FDA in the last 10 years in support of potential marketing approval for prostate cancer (PCa) prevention or treatment. METHODS: Patient demographic data were obtained from archival data sets of large registration trials submitted to the FDA to support proposed PCa indications. Six countries/regions were analyzed: the United States, Canada, Australia, Europe, the United Kingdom, and Eastern Europe. Background racial demographics were collected from national census data. RESULTS: Seventeen key PCa clinical trials were analyzed. These trials were conducted in the past 20 years, comprising 39,574 patients with known racial information. Most patients were enrolled in the United States, but there appeared to be a trend toward increased non-US enrollment over time. In all countries, racial minorities were generally under-represented. There was no significant improvement in racial minority enrollment over time. The United States enrolled the largest nonwhite population (7.1%). CONCLUSIONS: Over the past 20 years, racial minorities were consistently under-represented in key PCa trials. There is a need for effective measures that will improve enrollment of racial minorities. With increased global enrollment, drug developers should aim to recruit a patient population that resembles the racial demographics of the patient population to which drug use will be generalized upon approval.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".