A population-based analysis of germline BRCA1 and BRCA2 testing among ovarian cancer patients in an era of histotype-specific approaches to ovarian cancer prevention
Bibliographic record
Abstract
BACKGROUND: Identifying female carriers of BRCA1 and BRCA2 mutations is imperative for prevention of ovarian cancer and breast cancer. There are five major histologic subtypes of ovarian cancer and high grade serous cancer (the most common) is reported in 75-100% of BRCA1 and BRCA2 mutation carriers. We examined histology-based referral to the Hereditary Cancer Program following an educational prevention campaign recommending BRCA1 and BRCA2 mutation screening for all high-grade serous cancer patients. METHODS: We conducted a population-based retrospective study in the province of British Columbia, Canada that included all patients visiting the Hereditary Cancer Program for genetic counselling for BRCA1 and BRCA2 mutation between 2001 and 2014. We examined the difference in rates of BRCA1 and BRCA2 testing between serous cancer patients and endometrioid and clear cell cancer patients using a differences in differences analysis. We also calculated the mean number of family members tested for every BRCA1 and BRCA2 identified ovarian cancer patient before and after the educational campaign. RESULTS: There were 5712 women tested for a BRCA1 and BRCA2 mutation at the HCP between 2001 and 2014, 887 of which had previously received a diagnosis of ovarian cancer. By 2013, 43% of serous cancer patients were being tested for BRCA1 and BRCA2 mutations compared with 20% of endometrioid and clear cell patients (p < 0.001). The mean number of family members tested for each BRCA1 and BRCA2 positive ovarian cancer patient increased after the educational campaign from 2.54 to 3.27 (p = 0.071), and the number of family members identified as BRCA positive also increased significantly. CONCLUSIONS: Recommendations for histology-based referral significantly increased the likelihood of serous cancer patients being tested for BRCA mutations. There was also an increase in the number of carrier tests performed for each BRCA1 and BRCA2 index ovarian cancer patient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".