Familial Prostate and Breast Cancer in Men Treated with Prostatectomy for Prostate Cancer: A Population based Case-Control Study
Bibliographic record
Abstract
PURPOSE: We assessed familial prostate and breast cancer in Quebec. MATERIALS AND METHODS: Using a self-administered mail survey we assessed the prevalence of prostate and breast cancer in first degree relatives of 1,633 men treated with prostatectomy for prostate cancer in the province of Quebec and in first degree relatives of 1,386 spouse controls. RESULTS: The OR of familial breast cancer was 1.1 (95% CI 0.9 to 1.4). The OR of 3.0 (95% CI 2.5 to 3.6) recorded for prostate cancer was modified by francophone versus anglophone linguistic preference (OR 3.2, 95% CI 2.6, 3.9 versus 1.5, 95% CI 0.8 to 2.7, p = 0.02). Male sibship size was a statistically significant parameter modifying this association (p = 0.02), namely no brothers (OR 1.7, 95% CI 1.0 to 2.8), 1 or 2 (OR 3.1, 95% CI 2.2 to 4.3) and 3 or more (OR 3.9, 95% CI 2.9 to 5.2). Geographic regions of the province including and neighboring greater Montreal showed a lower OR than more peripheral regions (2.5, 95% CI 2.0 to 3.2 versus 4.1, 95% CI 2.9 to 5.7, p = 0.02). CONCLUSIONS: Francophone men with large male sibships residing in remote areas may be at higher risk for familial prostate cancer and represent the ideal target for further efforts to determine the genetic component of prostate cancer in Quebec.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".