Genetic assessment of breast cancer risk in primary care practice
Bibliographic record
Abstract
Family history is increasingly important in primary care as a means to detect candidates for genetic testing or tailored prevention programs. We evaluated primary care physicians' skills in assessing family history for breast cancer risk, using unannounced standardized patient (SP) visits to 86 general internists and family medicine practitioners in King County, WA. Transcripts of clinical encounters were coded to determine ascertainment of family history, risk assessment, and clinical follow-up. Physicians in our study collected sufficient family history to assess breast cancer risk in 48% of encounters with an anxious patient at moderate risk, 100% of encounters with a patient who had a strong maternal family history of breast cancer, and 45% of encounters with a patient who had a strong paternal family history of breast and ovarian cancer. Increased risk was usually communicated in terms of recommendations for preventive action. Few physicians referred patients to genetic counseling, few associated ovarian cancer with breast cancer risk, and some incorrectly discounted paternal family history of breast cancer. We conclude that pedigree assessment of breast cancer risk is feasible in primary care, but may occur consistently only when a strong maternal family history is present. Primary care education should focus on the link between inherited breast and ovarian cancer risk and on the significance of paternal family history. Educational efforts may be most successful when they emphasize the value of genetic counseling for individuals at risk for inherited cancer and the connection between genetic risk and specific prevention measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".