Year 1: Experiences of a tertiary cancer centre following implementation of reflex BRCA1 and BRCA2 tumor testing for all high-grade serous ovarian cancers in a universal healthcare system
Bibliographic record
Abstract
OBJECTIVE: This study compares the rate and time to genetic referral, and patient uptake of germline genetic services, before and after implementation of reflex BRCA1/2 tumor testing for high-grade serous ovarian cancer (HGSOC) in a universal healthcare system. METHODS: A retrospective chart review of HSGOC patients diagnosed in the year before (PRE) and after (POST) implementation of reflex BRCA1/2 tumor testing was conducted. Clinical information (date/age at diagnosis, personal/family history of breast/ovarian cancer, cancer stage, primary treatment, tumor results) and dates of genetics referral, counseling, and germline testing were obtained. Incident rate ratios (IRR) and 95% CI were calculated using negative binomial regression. Time to referral was evaluated using Kaplan-Meier survival analysis. Fisher Exact tests were used to evaluate uptake of genetic services. RESULTS: 175 HGSOC patients were identified (81 PRE; 94 POST). Post-implementation of tumor testing, there was a higher rate of genetics referral (12.88 versus 7.10/1000 person-days; IRR = 1.60, 95% CI: 1.07-2.42) and a shorter median time from diagnosis to referral (59 days PRE, 33 days POST; p = .04). In the POST cohort, most patients were referred prior to receiving their tumor results (n = 63/77; 81.8%). Once referred, most patients attended genetic counseling (94.5% PRE, 97.6% POST; p = .418) and pursue germline testing (98.6% PRE; 100% POST; p = .455). CONCLUSIONS: Following implementation of reflex BRCA1/2 tumor testing for HGSOC, significant improvements to the rate and time to genetics referral were identified. Additional studies are needed to evaluate physician referral practices and the long-term impact of reflex tumor testing.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".