Direct Genetics Referral Pathway for High-Grade Serous Ovarian Cancer Patients: The “Opt-Out” Process
Bibliographic record
Abstract
Purpose. In order to meet a clinical need for better pathways to access genetic testing for ovarian cancer patients, we implemented and reviewed an opt-out referral process for genetic consultation whereby a referral is automatically sent to genetics following a pathological diagnosis of HGSC. Methods. Following implementation of the opt-out referral process, each month a list of new cases of HGSC was generated from the synoptic pathology report and forwarded directly to the Cancer Genetics clinic. Using an advanced directive, patients were automatically referred for genetic counselling two months after surgery. If the patient declined genetic counselling (opted-out) after discussion with their surgeon within the two months after surgery, the Genetic Counsellor was informed and the patient was removed from the referral process. Results. Between January 1, 2015, and December 31, 2017, 168 women were diagnosed with HGSC, of whom 167 received a referral for genetic consultation. In only one case the referral was cancelled by the surgeon, resulting in a referral rate of 99.4%. By the end of the study period, 133 women attended a genetics consultation appointment and 125 (94%) agreed to proceed with genetic testing. Among those who completed genetic testing, 15% tested positive for a BRCA1 or BRCA2 gene mutation. Of the women who tested positive for a BRCA1/2 mutation, 56% had no family history of breast or ovarian cancer. Conclusions. The opt-out referral process described in this study is s a feasible, effective, and patient-centred approach to increase access to BRCA1/2 testing for patients with ovarian cancer.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".