Patient Experience with a Gynecologic Oncology-Initiated Genetic Testing Model for Women with Tubo-Ovarian Cancer
Bibliographic record
Abstract
BACKGROUND: ). Identifying these variants can help to inform eligibility for therapies, guide surveillance and prevention of new primary cancers, and assess risk to family members. The Gynecologic Oncology-Initiated Genetic Testing Model (GOIGT) was initiated at the McGill University Health Centre (MUHC) to streamline universal germline genetic testing for this population, while addressing the limited resources in the public healthcare system. This study aimed to evaluate the patient experience of participating in this model. METHODS: Study participants were patients diagnosed with high-grade non-mucinous epithelial tubo-ovarian cancer who underwent genetic testing through the GOIGT model between 1 January 2017 and 31 December 2020. Eligible participants completed the retrospective questionnaires at least one month after result disclosure. RESULTS: A total of 126 patients were tested through the GOIGT model during the study period, of which 56 were invited to participate. Thirty-four participants returned the study questionnaire. Overall, participants did not report decision regret following the genetic testing and were satisfied with the GOIGT model. Participants reported low levels of uncertainty and distress related to the implications of their test results for themselves and their family members. CONCLUSIONS: The results of this study support the continued implementation of mainstreamed genetic testing models for women with high-grade non-mucinous tubo-ovarian cancer. Further studies are required to compare experiences for patients with different genetic test results.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".