Rates of genetic consultation in high‐grade serous ovarian cancer patients in the era of PARP inhibitor therapy: A population‐based study
Bibliographic record
Abstract
OBJECTIVE: The American Society of Clinical Oncology recommends all patients with high-grade serous ovarian carcinoma (HGSC) undergo germline genetic testing. Genetic consultation rates in Ontario, Canada, only reached 13.3% in 2011. In 2016, PARP inhibitor maintenance therapy became available in Ontario for BRCA-positive HGSC patients. Given expanding treatment options, we re-examined genetic consultation rates among HGSC patients. METHODS: This retrospective cohort study identified patients diagnosed with HGSC between 2012 and 2019 using population-based administrative data from Ontario. Genetics consultations were identified using Ontario Health Insurance Plan billing codes. Consultation rates over time were analyzed using Cochran-Armitage trend test and segmental regression analysis. Multivariable analysis identified factors associated with attending genetics consultation. RESULTS: This study included 4645 HGSC patients. The mean age was 64.2 years (±SD 12.3); 56.3% had stage 3-4 disease. Overall, approximately 35% attended genetics consultations. The genetic consultation rate per year increased significantly from 21.6% to 42.6% (P < 0.001). Shorter times between diagnosis and genetics consult were observed after PARP inhibitors became available (68.1 vs 34.1 weeks, P < 0.001). Patients treated at designated cancer centers (odds ratio [OR] 2.11, P < 0.001), diagnosed in later years (OR 1.33, P < 0.001), and from higher income groups (P < 0.05) were more likely to attend genetics consultation; older patients were less likely (OR 0.98, P < 0.001). After PARP inhibitors became available, consultation rates plateaued (P < 0.001). CONCLUSIONS: Between 2012 and 2019, genetic consultation rates improved significantly among HGSC patients; however, a large proportion of patients never attended consultation. Further exploration of barriers to care is warranted to improve consultation rates and ensure equitable access to care.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".