Genetic testing women with newly diagnosed breast cancer: What criteria are the most predictive of a positive test?
Bibliographic record
Abstract
BACKGROUND: Knowledge of pathogenic variants in cancer-predisposing genes is important when making breast cancer treatment decisions, but genetic testing is not universal and criteria must be met to qualify for genetic testing. The objective of this study was to evaluate the pathogenic variant yield for nine cancer predisposition genes by testing criteria, singly and in combination. METHODS: Women diagnosed with breast cancer between June 2013 and May 2018 were recruited from four centers in Toronto, Canada. Participants completed a demographics and family history questionnaire and clinical characteristics were collected from medical charts. Genetic testing was done for BRCA1, BRCA2, PALB2, ATM, CHEK2, BRIP1, RAD51D, RECQL, and TP53. Pathogenic variant frequencies were calculated according to five criteria (age ≤ 50, triple-negative breast cancer, family history, bilateral breast cancer, or Jewish ethnicity). RESULTS: Of the 1006 women studied, 100 women (9.9%) were found to have a pathogenic variant in one of the nine genes tested. The highest prevalence of pathogenic variants was found in women with triple-negative breast cancer (23%). Of the 100 pathogenic variants detected, 78 were detected in women diagnosed at age 50 or less. A total of 96% of the mutations were identified with three criteria (age of diagnosis, family history, and triple-negative status). CONCLUSIONS: Genetic testing criteria for women with breast cancer should include women with triple-negative breast cancer, regardless of age. All women aged 50 years or below at time of breast cancer diagnosis should be offered genetic testing.
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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 | 0.000 | 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".