Universal access to genetic counseling for women with epithelial ovarian cancer in Nova Scotia: Evaluating a new collaborative care model
Bibliographic record
Abstract
Women with pathogenic variants in BRCA1/2 have a significantly increased lifetime risk of breast and ovarian cancers. The availability of genetic testing to identify BRCA1/2 carriers is imperative to disease prevention and treatment. We evaluated the effectiveness of a new collaborative care model in Nova Scotia, involving the integration of genetic counselors into tumor board rounds, reduction in time allotted for initial genetic counseling appointments from 60 to 45 min, and a standardized dictation template, to increase referral rate for genetic counseling. We also assessed the study cohorts' preferences on timing for genetic testing. A retrospective chart review was performed on all women diagnosed with epithelial ovarian cancer (EOC) from 2012 to 2017 (N = 386). Pertinent clinical outcomes were categorized and wait times to different nodes of the clinical pathway assessed. A questionnaire was sent to this same cohort of women to identify preference for the timing of genetic testing (n = 103). The chi-square and Wilcoxon's rank-sum tests were used to compare demographic and clinical variables pre- and post-care model implementation. We identified a 48.2% (95% CI: 39.4-56.7, p < .001) increase in referral for genetic counseling following implementation of the new care model. Median time from diagnosis to referral decreased by 74.0 days (p < .001) and median time from referral to first appointment by 54.0 days (p < .001). 56.3% of women desired referral at the time of diagnosis. This care model for women newly diagnosed with EOC in Nova Scotia was successful in increasing referral rates for genetic counseling. Majority of women pursued genetic testing following and favored that referral for genetic counseling be made at the time of diagnosis, highlighting the importance for timely access.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".