Identification and management of women with a family history of breast cancer: Practical guide for clinicians.
Bibliographic record
Abstract
OBJECTIVE: To summarize the best evidence on strategies to identify and manage women with a family history of breast cancer. SOURCES OF INFORMATION: A PubMed search was conducted using the search terms breast cancer, guidelines, risk, family history, management, and magnetic resonance imaging screening from 2000 to 2016. Most evidence is level II. MAIN MESSAGE: Taking a good family history is essential when assessing breast cancer risk in order to identify women suitable for referral to a genetic counselor for possible genetic testing. Offering risk-reducing surgery (bilateral prophylactic mastectomy, bilateral salpingo-oophorectomy) to women with BRCA genetic mutations can save lives. All women with a family history of breast cancer should be encouraged to stay active and limit alcohol intake to less than 1 drink per day; some will qualify for chemoprevention. Women with a 20% to 25% or greater lifetime risk of breast cancer should be offered enhanced screening with annual magnetic resonance imaging in addition to mammography. CONCLUSION: Healthy living and chemoprevention (for suitable women) could reduce breast cancer incidence; enhanced screening could result in earlier detection. Referring women who carry BRCA mutations for risk-reducing surgery will save lives.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".