Building the What Comes Next Cohort for <i>BRCA1</i> and <i>BRCA2</i> testing: a descriptive analysis
Bibliographic record
Abstract
Background: Our understanding of how testing for and mutations of the BRCA1 and BRCA2 genes affect cancer risk and the use of risk-reduction strategies comes largely from studies of women recruited from specialized genetics clinics. Our aim was to assemble a generalizable cohort of women who underwent BRCA1/BRCA2 testing (the What Comes Next Cohort), irrespective of test result, to enable study of health care utilization and outcomes after testing. Methods: This descriptive study included adult women (≥ 18 yr) who met at least 1 of 13 provincial criteria for BRCA1/BRCA2 testing and who underwent genetic testing at sites in Ontario, Canada, from 2007 to 2016. Most of the women were tested at 1 of 2 main sites, which together capture about 70% of all BRCA1/BRCA2 testing in the province. We collected detailed demographic, genetic testing and family history data through chart review for linkage with data from administrative health databases providing information on cancer history before and after testing. We followed all women to September 2019, evaluating the demographic characteristics of the cohort, indications for testing and test results. Results: We identified 15 986 women (mean age 52.5 [standard deviation 13.9] yr) who underwent BRCA1/BRCA2 testing. Of these, 2033 women had positive results, 1175 women had variants of uncertain significance, and 12 778 women had negative results. Positive yields were 41.0% (955/2329) for predictive testing (for familial variants), 10.4% (216/2072) for Ashkenazi Jewish founder testing and 7.4% (862/11 585) for complete gene analysis. Six of the 13 provincial testing criteria had less than 10% positive yield. Among 403 women who tested negative for Ashkenazi Jewish founder mutations and subsequently underwent complete gene analysis, 12 (3.0%) tested positive for alternate pathogenic or likely pathogenic variants in the BRCA1 or BRCA2 gene. Interpretation: Several provincial eligibility criteria for BRCA1/BRCA2 testing led to positive results in less than 10% of cases. How testing influences women’s health care behaviours, particularly those with negative results and those found to carry variants of uncertain significance, is unknown; the What Comes Next Cohort will be instrumental in the study of long-term implications of BRCA1/BRCA2 testing.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".