Referral, Genetic Counselling, and BRCA Testing in the Manitoba High-Grade Serous Ovarian Cancer Population, 2004–2019
Bibliographic record
Abstract
(1) Background: The primary objective of this study was to examine the rate of genetic referral, BRCA testing, and BRCA positivity amongst all patients with high-grade serous ovarian cancers (HGSOC) from 2004–2019. The secondary objective was to analyze secondary factors that may affect the rates of referral and testing. (2) Methods: This population-based cohort study included all women diagnosed with HGSOC using the Manitoba Cancer Registry, CervixCheck registry, Medical Claims database at Manitoba Health, the Hospital Discharge abstract, the Population Registry, and Winnipeg Regional Health Authority genetics data. Data were examined for three different time cohorts (2004–2013, 2014–2016; 2017–2019) correlating to practice pattern changes. (3) Results: A total of 944 patients were diagnosed with HGSOC. The rate of genetic referrals changed over the three timeframes (20.0% → 56.7% → 36.6%) and rate of genetic testing increased over the entire timeframe. Factors found to increase rates of referral and testing included age, histology, history of oral contraceptive use, and family history of ovarian cancer. Prior health care utilization indicators did not affect genetic referral or testing. (4) Conclusion: The rate of genetic referral (2004–2016) and BRCA1/2 testing (2004–2019) for patients with a diagnosis of HGSOC increased over time. A minority of patients received a consultation for genetics counselling, and even fewer received testing for a BRCA1/2. Without a genetic result, it is difficult for clinicians to inform treatment decisions. Additional efforts are needed to increase genetics consultation and testing for Manitoban patients with HGSOC. Effects of routine tumour testing on rates of genetic referral will have to be examined in future studies.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".