Time to decide about risk-reducing mastectomy: A case series of BRCA1/2 gene mutation carriers
Bibliographic record
Abstract
BACKGROUND: The purpose of this research was to explore women's decision-making experiences related to the option of risk-reducing mastectomy (RM), using a case series of three women who are carriers of a BRCA1/2 gene mutation. METHODS: Data was collected in a pilot study that assessed the response of women to an information booklet about RM and decision-making support strategies. A detailed analysis of three women's descriptions of their decision-making processes and outcomes was conducted. RESULTS: All three women were carriers of a BRCA1/2 gene mutation and, although undecided, were leaning towards RM when initially assessed. Each woman reported a different RM decision outcome at last follow-up. Case #1 decided not to have RM, stating that RM was "too radical" and early detection methods were an effective strategy for dealing with breast cancer risk. Case #2 remained undecided about RM and, over time, she became less prepared to make a decision because she felt she did not have sufficient information about surgical effects. Case #3 had undergone RM by the time of her second follow-up interview and reported that she felt "a load off (her) mind now". CONCLUSION: RM decision making may shift over time and require decision support over an extended period.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".