Breast and Ovarian Cancer: The Forgotten Paternal Contribution
Bibliographic record
Abstract
Five to 10% of all cases of breast and ovarian cancer are attributed to a heritable genetic predisposition. Transmission of BRCA1 and BRCA2 mutations is equally likely through maternal or paternal lineage; however, fewer referrals to cancer genetics clinics appear to be made for a paternal, than maternal, family history of breast and/or ovarian cancer. To examine this potential bias, a retrospective review of 315 patient and family charts was conducted by one familial cancer clinic in Toronto, Canada. Referral letters, risk estimates, and family histories were analyzed to identify significant differences between patients referred with maternal and paternal family histories. It was determined that patients are approximately five times more likely to be referred with a maternal family history of breast and/or ovarian cancer as compared to those with a paternal family history (p = <.0001). Individuals with a paternal family history were found to have a different, and higher, pattern of risk estimates (p = .00064). No significant difference was seen between the type of referrals sent by general practitioners, oncologists, and gynecologists. Recommendations to increase the awareness of paternal family history in assessing cancer risk are provided.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".