A Guide to Establishing the Risk for Breast Cancer in the Plastic Surgery Patient
Bibliographic record
Abstract
Plastic surgeons with a practice that includes breast augmentation, breast reduction, and breast reconstruction should have a working knowledge of both genetic and environmental breast cancer risk factors and of risk stratification of these patients. Specific tests to determine genetic susceptibility to certain cancers have become increasingly prevalent during recent years. Such testing is carried out by multidisciplinary teams, which may include plastic surgeons. Patients with a very strong family history of positive genetic testing for BRCA1 or BRCA2 may be offered prophylactic mastectomies. The authors present an overview of risk factors so that surgeons can stratify women appropriately with respect to their breast cancer risk. In addition to a brief literature review, the authors present 2 patients who represent the type of personal and family history of breast and/or ovarian cancer, and environmental risk factors that one would typically encounter in a plastic surgery patient population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".