Cost-effectiveness of a 21-gene recurrence score assay versus Canadian clinical practice in women with early-stage estrogen- or progesterone-receptor-positive, axillary lymph-node negative breast cancer
Bibliographic record
Abstract
BACKGROUND: A 21-gene recurrence score (RS) assay may inform adjuvant systematic treatment decisions in women with early stage breast cancer. We sought to investigate the cost effectiveness of using the RS-assay versus current clinical practice (CCP) in women with early-stage estrogen- or progesterone-receptor-positive, axilliary lymph-node negative breast cancer (ER+/ PR + LN- ESBC) from the perspective of the Canadian public healthcare system. METHODS: We developed a Markov model to project the lifetime clinical and economic consequences of ESBC. We evaluated adjuvant therapy separately in post- and pre-menopausal women with ER+/ PR + LN- ESBC. We assumed that the RS-assay would reclassify pre- and post-menopausal women among risk levels (low, intermediate and high) and guide adjuvant systematic treatment decisions. The model was parameterized using 7 year follow up data from the Manitoba Cancer Registry, cost data from Manitoba administrative databases, and secondary sources. Costs are presented in 2010 CAD. Future costs and benefits were discounted at 5%. RESULTS: The RS-assay compared to CCP generated cost-savings in pre-menopausal women and had an ICER of $60,000 per QALY gained in post-menopausal women. The cost effectiveness was most sensitive to the proportion of women classified as intermediate risk by the RS-assay who receive adjuvant chemotherapy and the risk of relapse in the RS-assay model. CONCLUSIONS: The RS-assay is likely to be cost effective in the Canadian healthcare system and should be considered for adoption in women with ER+/ PR + LN- ESBC. However, ongoing assessment and validation of the assay in real-world clinical practice is warranted.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".