Cancer risk management strategies and perceptions of unaffected women 5 years after predictive genetic testing for BRCA1/2 mutations
Bibliographic record
Abstract
In a French national cohort of unaffected females carriers/non-carriers of a BRCA1/2 mutation, long-term preventive strategies and breast/ovarian cancer risk perceptions were followed up to 5 years after test result disclosure, using self-administered questionnaires. Response rate was 74%. Carriers (N=101) were younger (average age ± SD=37 ± 10) than non-carriers (N=145; 42 ± 12). There were four management strategies that comprised 88% of the decisions made by the unaffected carriers: 50% opted for breast surveillance alone, based on either magnetic resonance imaging (MRI) and other imaging (31%) or mammography alone (19%); 38% opted for either risk reducing salpingo-oophorectomy (RRSO) and breast surveillance, based on MRI and other imaging (28%) or mammography alone (10%). The other three strategies were: risk reducing mastectomy (RRM) and RRSO (5%), RRM alone (2%) and neither RRM/RRSO nor surveillance (6%). The results obtained for various age groups are presented here. Non-carriers often opted for screening despite their low cancer risk. Result disclosure increased carriers' short-term high breast/ovarian cancer risk perceptions (P ≤ 0.02) and decreased non-carriers' short- and long-term perceptions (P<0.001). During follow-up, high breast cancer risk perceptions increased with time among those who had no RRM and decreased in the opposite case; high ovarian cancer risk perceptions increased further with time among those who had no RRSO and decreased in the opposite case; RRSO did not affect breast cancer risk perceptions. Informed decision-making involves letting women know whether opting for RRSO and breast MRI surveillance is as effective in terms of survival as RRM and RRSO.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".