The Role of the Surgeon in the Germline Testing of the Newly Diagnosed Breast Cancer Patient
Bibliographic record
Abstract
For patients with newly diagnosed breast cancer, information regarding hereditary predisposition can influence treatment decisions. From a surgical standpoint, patients with known germline mutations may alter decisions of local therapy to reduce the risk of second breast primaries. This information may also be considered in the choice of adjuvant therapies or eligibility for clinical trials. In recent years, the criteria for the consideration of germline testing in patients with breast cancer has expanded. Additionally, studies have shown a similar prevalence of pathogenic mutations in those patients outside of these traditional criteria, prompting calls for genetic testing for all patients with a history of breast cancer. While data confirms the benefit of counseling by certified genetics professionals, the capacity of genetic counselors may no longer meet the needs of these growing numbers of patients. National societies assert that counseling and testing can be performed by providers with training and experience in genetics. Breast surgeons are well positioned to offer this service, as they receive formal genetics training during their fellowship, manage these patients daily in their practices, and are often the first providers to see patients after their cancer diagnosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".