Cost–Utility Analysis of 21-Gene Assay for Node-Positive Early Breast Cancer
Bibliographic record
Abstract
Background: For women with lymph node (ln)-positive, estrogen receptor-positive, and her2 (human epidermal growth factor receptor 2)-negative breast cancer (bca), current guidelines recommend treatment with both hormonal therapy and chemotherapy. The 21-gene Recurrence Score (rs) assay might be helpful in selecting patients with bca who can be spared chemotherapy when they have 1-3 positive lns and a lower risk of recurrence. In the present study, we performed a cost-utility analysis comparing use of the 21-gene rs assay with current practice from the perspective of a Canadian health care payer. Methods: A Markov model was developed to determine costs and quality-adjusted life-years (qalys) over a patient's lifetime. Patient outcomes in both study groups were examined based on published clinical trials. Costs were derived primarily from published Canadian sources. Costs and outcomes were discounted at 1.5% annually, and costs are reported in 2016 Canadian dollars. A probabilistic analysis was used, and the model parameters were varied in a sensitivity analysis. Results: The results indicate that use of the 21-gene rs assay was less costly ($432 less) and more effective (0.22 qalys) than current practice. The probabilistic analysis revealed that 70% of the 10,000 simulated incremental cost-effectiveness ratios were in the southeast quadrant. The results were sensitive to the probability of a low rs and to the probability of receiving chemotherapy in the low-risk rs category and in current practice. Conclusions: Use of the 21-gene rs assay could be a cost-effective strategy for Ontario patients with estrogen receptor-positive, her2-negative early bca and 1-3 positive lns.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".