Trends in the Utilization of BRCA1 and BRCA2 Testing After the Introduction of a Publicly Funded Genetic Testing Program
Bibliographic record
Abstract
Purpose: To effectively reduce cancer burden, genetic testing programs should identify high-risk individuals prior to cancer development, when risk-reduction strategies can be implemented. We evaluated trends in BRCA1/BRCA2 testing use after implementation of a publicly funded testing program. Methods: We conducted a retrospective, near population-based study of women who underwent BRCA1/BRCA2 testing in Ontario, Canada, (2007–2016) (n = 15,986). Temporal trends were evaluated using linear and Poisson regression. Results: Although annual utilization of testing increased over time (p < 0.001), mean age at testing increased from 49.9 years (SD 13.8) in 2007 to 53.8 years (SD 13.7) in 2016 (p < 0.001). The proportion of women with a cancer history at testing also increased from 53.5% in 2007 to 66.3% in 2015 (p < 0.001); the proportion of women free from breast cancer did not change significantly (49.2% in 2007 versus 45.1% in 2015, p = 0.90). As a proportion of all tested, those with breast cancer tested within 3 months of diagnosis increased over time (0.39% of tests in 2007 versus 13.6% of tests in 2015; p < 0.001). Conclusions: While the institution of a publicly funded genetic testing program was associated with rising utilization, increasing age at testing and decreasing testing of unaffected women suggest limitations in identifying high-risk individuals eligible for risk-reduction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".