Family history as a predictor of uptake of cancer preventive procedures by women with a <i>BRCA1</i> or <i>BRCA2</i> mutation
Bibliographic record
Abstract
Women with a BRCA1 or BRCA2 mutation are at an elevated risk of developing breast and ovarian cancer; however, it is unclear to what extent family history influences the uptake of cancer prevention options. Women with a BRCA1/2 mutation completed a follow-up questionnaire that assessed uptake of cancer preventive options. The pedigree of each woman was reviewed, and information was recorded on cancers diagnosed in relatives. Five hundred and seventeen women were included in the study. Women with a sister with breast cancer were more likely to have a prophylactic mastectomy than those without a sister with breast cancer [odds ratios (OR) = 2.4, p = 0.003]. Uptake of prophylactic mastectomy was significantly lower in women with a mother with ovarian cancer compared with those whose mother did not have ovarian cancer (OR = 0.4, p = 0.01). Having a mother or sister with ovarian cancer significantly predicted the uptake of prophylactic oophorectomy (OR = 1.6, p = 0.04). Women with a BRCA2 mutation were less likely to have a prophylactic oophorectomy than those with a BRCA1 mutation (OR = 0.49, p = 0.0004). Among women with a BRCA1 or BRCA2 mutation, family history predicts the uptake of prophylactic mastectomy and prophylactic oophorectomy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".