Recognizing BRCA gene mutation risk subsequent to breast cancer diagnosis in southwestern Ontario.
Bibliographic record
Abstract
OBJECTIVE: To describe the population of women in southwestern Ontario who were diagnosed with potentially preventable BRCA mutation-related breast cancer. DESIGN: Retrospective chart review. SETTING: The Cancer Genetics Clinic of the London Regional Cancer Program in London, Ont. PARTICIPANTS: Patients younger than 52 years of age who were referred to the London Regional Cancer Program Cancer Genetics Clinic between 1997 and 2007 for BRCA testing after being diagnosed with breast cancer (N = 1017). MAIN OUTCOME MEASURES: The proportion of women with BRCA1 or BRCA2 gene mutations and the proportion of women who would have qualified, based on family cancer history, for referral for genetic counseling and testing before their breast cancer diagnoses. RESULTS: Among the 1017 women referred for BRCA testing, 63 women younger than 52 years of age who had been diagnosed with breast cancer were found, subsequent to this diagnosis, to have BRCA1 or BRCA2 gene mutations. Of these, 41 (65%) had family cancer histories that would have qualified them for genetic counseling and testing, according to provincial criteria, before their own breast cancer diagnoses. Of the 63 women, most (81%) had been referred for BRCA gene mutation testing by their oncologists or surgeons. CONCLUSION: Our results suggest that the diagnosis of breast cancer could have been anticipated, and perhaps in some cases prevented, in up to two-thirds of high-risk women younger than 52 years of age in southwestern Ontario. If the high-risk status of these women had been recognized, they might have had the opportunity to choose genetic counseling, testing, more effective cancer surveillance, and potentially preventive options. The results of this study call for increased public and care provider awareness about hereditary breast cancer risk to promote women's ability to choose to access genetic counseling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".