Reasons for risk-reducing mastectomy versus MRI-screening in a cohort of women at high hereditary risk of breast cancer
Bibliographic record
Abstract
OBJECTIVE: To determine the reasons that motivate women in a cohort of women under intensive surveillance for breast cancer to undergo risk-reducing mastectomy (RRM). PATIENTS AND METHODS: Women with a BRCA1 or BRCA2 mutation who were enrolled in an MRI-based breast screening study were eligible to participate in this survey. A self-administered questionnaire was given to women who did, and who did not terminate annual MRI-based surveillance in order to undergo RRM. The questionnaire included information on family history, risk perception and satisfaction with screening. In addition, women were asked to provide the principal reason for their choice of having preventive surgery or not, and were asked about their satisfaction with this choice. RESULTS: 246 women without breast cancer participated in the study. Of these, 39 women (16%) elected to have RRM at some point after initiating screening. Although women who had a mother or sister with breast cancer were more likely to opt for RRM than were women with no affected first-degree relative (21% versus 10%) this did not reach statistical significance. Women who perceived their breast cancer risk to be greater than 50% were more likely to opt for RRM than were women who estimated their risk to be less than 50% (19% versus 6%). Fear of cancer was the most common reason cited for choosing to have RRM (38% of respondents) followed by having had a previous cancer, (25%), then concern over their children (16%). CONCLUSION: Among women with a BRCA mutation who are enrolled in an MRI-based screening program, a high perception of personal breast cancer risk and a history of breast cancer in a first-degree relative are predictors of the decision to have RRM.
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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 | 0.001 |
| 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 | 0.001 | 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".