Family history of prostate cancer and prostate tumor aggressiveness in black and non-black men;results from an equal access biopsy study
Bibliographic record
Abstract
PURPOSE: To test for racial differences in associations between family history (FH) of prostate cancer (PC) and prostate cancer aggressiveness in a racially diverse equal access population undergoing prostate biopsy. SUBJECTS/PATIENTS AND METHODS: We prospectively enrolled men undergoing prostate biopsy at the Durham Veterans Administration from 2007 to 2018 and assigned case or control status based on biopsy results. Race and FH of PC were self-reported on questionnaires. Logistic regression was used to test the association between FH and PC diagnosis overall and by tumor aggressiveness [high- (Grade Group 3-5) or low-grade (Grade Group 1-2) vs. no cancer], overall, and stratified by race. Models were adjusted for age and year of consent, race, PSA level, digital rectal exam findings, prostate volume, and previous (negative) biopsy receipt. RESULTS: Of 1,225 men, 323 had a FH of PC and 652 men were diagnosed with PC on biopsy. On multivariable analysis, FH was associated with increased odds of high-grade PC in black (OR 1.85, p = 0.041) and all men (OR 1.56, p = 0.057) and was unrelated to overall or low-grade PC diagnosis, overall, or stratified by race (all p ≥ 0.325). In sensitivity analyses among men without a previous biopsy, results were slightly more pronounced. CONCLUSION: In this setting of equal access to care, positive FH of PC was associated with increased tumor aggressiveness in black men, but not non-black men undergoing prostate biopsy. Further research is required to tease apart the contribution of genetics from increased PC awareness potentially influencing screening and biopsy rates in men with FH.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".