A Literature Review of Racial Disparities in Prostate Cancer Research
Bibliographic record
Abstract
Background: Despite recent awareness of institutional racism, there are still important racial disparities in prostate cancer medical research. We investigated the historical development of research on racial disparities and bias. Methods: PubMed was searched for the term ‘prostate cancer race’ and added key terms associated with racial disparity. As an indicator of scientific interest in the topic, we analyzed whether the number of publications increased linearly as an indicator of growing interest. The linearity is expressed as R2. Results: The general search term “prostate cancer race” yielded 4507 publications. More specific search terms with ≥12 publications showing a higher scientific interest were found after 2005. The terms with the most publications when added to the general term were “genetic” (n = 1011), “PSA” (n = 995), and “detection” (n = 861). There was a linear increase in publications for “prostate cancer race” (R2 = 0.75) since 1980. Specific terms added to the general terms with a high linear increase (R2 ≥ 0.7) were “screening” (R2 = 0.82), “detection” (R2 = 0.72), “treatment access” (R2 = 0.71), and “trial underrepresentation” (R2 = 0.71). However, only a few studies have investigated its association with sexual activity. A combination with “sexual” showed 157 publications but only two years with ≥12 publications/year. Conclusion: The terms “genetic”, “PSA”, and “detection” have been the focus of recent research on racial differences in prostate cancer. We found that old stereotypes are still being mentioned but seem to find little interest in the current literature. Further research interest was found in “treatment access”. Recently, interest in socioeconomic factors has decreased.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".