Multigene assays and molecular markers in breast cancer: systematic review of health economic analyses
Bibliographic record
Abstract
Breast cancer is the most common female cancer and is associated with a significant clinical and economic burden. Multigene assays and molecular markers represent an opportunity to direct chemotherapy only to patients likely to have significant benefit. This systematic review examines published health economic analyses to assess the support for adjuvant therapy decision making. Literature searches of PubMed, the Cochrane Library, and congress databases were carried out to identify economic evaluations of multigene assays and molecular markers published between 2002 and 2012. After screening and data extraction, study quality was assessed using the Quality of Health Economic Studies instrument. The review identified 29 publications that reported evaluations of two assays: Oncotype DX(®) and MammaPrint. Studies of both tests provided evidence that their routine use was cost saving or cost-effective versus conventional approaches. Benefits were driven by optimal allocation of adjuvant chemotherapy and reduction in chemotherapy utilization. Findings were sensitive to variation in the frequency of chemotherapy prescription, chemotherapy costs, and patients' risk profiles. Evidence suggests that multigene assays are likely cost saving or cost-effective relative to current approaches to adjuvant therapy. They should benefit decision making in early-stage breast cancer in a variety of settings worldwide.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".