Breast cancer genetic mutation: Synthesis of women's experience
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To systematically identify and synthesise the experiences reported by women with a breast cancer mutation who do not have cancer as reported in qualitative research published between 2013 and 2020. BACKGROUND: Women carrying a BReast CAncer (BRC) genetic mutation have an increased risk for breast and ovarian cancer. They must engage in emotional decision-making regarding risk management strategies to prevent cancer, including risk-reducing bilateral mastectomy and bilateral salpingo-oophorectomy. DESIGN AND METHODS: The ENTREQ statement guided this review. Eight databases were systematically searched (CINAHL, Pubmed, Embase, Psychinfo [Ovid], Web of Science, Scopus, Proquest and Lenus). Synthesis was guided by "best fit" framework. The Critical Appraisal Skills Programme guided assessment of methodological limitations and confidence in the review findings was informed by GRADE-CERQual. RESULTS: Twenty studies met the inclusion criteria for synthesis. Six themes were synthesised from the included studies (anxiety; family planning; it's a family affair; empowerment; actions; pragmatic adjustments). CONCLUSIONS: The multidimensional experiences of women living with a BRCA1/2 mutation require an individualised response based on women's needs at their life stages. A decision coaching model adopted during consultations could support women to guide decision-making regarding cancer risk-reducing strategies.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".