Bibliographic record
Abstract
OBJECTIVE: To assist family physicians in evaluating patients' risk for hereditary ovarian cancer and to review strategies for preventing ovarian cancer. QUALITY OF EVIDENCE: The MEDLINE, EMBASE, CANCERLIT, and CINAHL databases were searched from 1970 to 1999 using key words related to hereditary ovarian cancer, screening, oral contraceptives, prophylactic oophorectomy, cancer worriers, satisfaction, and perceived risk. Recommendations in this paper are based on evidence from case-control and cohort studies and, where appropriate, consensus conferences. MAIN MESSAGE: Of all women who present with ovarian cancer, 20% have a family history of ovarian cancer and 8% carry a BRCA 1 or BRCA 2 mutation. Women who carry a BRCA 1 mutation have a 63% lifetime risk of developing ovarian cancer, and women who carry a BRCA 2 mutation have a 27% lifetime risk of developing ovarian cancer. Preventive strategies include screening (level 3 evidence for postmenopausal women and level 5 evidence for women with a family history of ovarian cancer), use of oral contraceptives (level 3 evidence for the general population and for mutation carriers), and prophylactic oophorectomy (level 3 evidence in first-degree relatives of patients with breast or ovarian cancer). CONCLUSION: Women who have a family history of ovarian cancer should be offered genetic counseling and discussion of various preventive strategies for minimizing their risk.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".