Canadian cost-effectiveness model of BRCA-driven surgical prevention of breast/ovarian cancers compared to treatment if cancer develops
Bibliographic record
Abstract
OBJECTIVES: To assess the cost effectiveness from a Canadian perspective of index patient germline BRCA testing and then, if positive, family members with subsequent risk-reducing surgery (RRS) in as yet unaffected mutation carriers compared with no testing and treatment of cancer when it develops. METHODS: A patient level simulation was developed comparing outcomes between two groups using Canadian data. Group 1: no mutation testing with treatment if cancer developed. Group 2: cascade testing (index patient BRCA tested and first-/second-degree relatives tested if index patient/first-degree relative is positive) with RRS in carriers. End points were the incremental cost-effectiveness ratio (ICER) and budget impact. RESULTS: There were 29,102 index patients: 2,786 ovarian cancer and 26,316 breast cancer (BC). Using the base-case assumption of 44 percent and 21 percent of women with a BRCA mutation receiving risk-reducing bilateral salpingo-oophorectomy and risk-reducing mastectomy, respectively, testing was cost effective versus no testing and treatment on cancer development, with an ICER of CAD 14,942 (USD 10,555) per quality-adjusted life-year (QALY), 127 and 104 fewer cases of ovarian and BC, respectively, and twenty-one fewer all-cause deaths. Testing remained cost effective versus no testing at the commonly accepted North American threshold of approximately CAD 100,000 (or USD 100,000) per QALY gained in all scenario analyses, and cost effectiveness improved as RRS uptake rates increased. CONCLUSIONS: Prevention via testing and RRS is cost effective at current RRS uptake rates; however, optimization of uptake rates and RRS will increase cost effectiveness and can provide cost savings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".