An evaluation of race-based representation among men participating in clinical trials for prostate cancer and erectile dysfunction
Bibliographic record
Abstract
Background: Inclusion of ethnic/racial minorities in clinical trials is essential to fully assess therapeutic efficacy. It is well-known that populations respond dissimilarly to interventions. Our objective is to analyze the inclusion of minority men in clinical trials for erectile dysfunction (ED). Methods: We searched ClinicalTrials.gov for the disease keyword: "Erectile Dysfunction" and used "Prostate Cancer" for comparison. Completed trials which reported demographic data were included for analysis. Literature was reviewed to determine the prevalence of ED and prostate cancer (PC) among Hispanic, Black, White, and Asian men. The proportion of individuals of each group that participated in trials is divided by the proportion of each group in the disease population to calculate the "Participation to Prevalence Ratio" (PPR). PPRs between 0.8 and 1.2 indicates adequate representation, <0.8 is under-representation and >1.2 is over-representation. Results: A total of 312 trials were assessed: 289 for prostate cancer and 23 for ED. Hispanic men comprised 11.8% of ED trial participants and 4.6% of prostate cancer trial participants, yet represented 18% of ED patients and 7.3% of PC patients. Black/African-American (AA) men accounted for 10.2% of ED trial participants and 9.4% of PC trial participants, but comprise 16% of ED patients, and 16.3% of PC patients. Hispanic and AA men are under-represented in trials for ED and Prostate Cancer (Hispanic ED PPR = 0.66; Hispanic PC PPR = 0.63; AA ED PPR = 0.64; AA PC PPR = 0.58). Conclusion: Our analysis shows that both Hispanic and AA men are underrepresented in both ED and PC clinical trials.
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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 | 0.216 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".