Population-Based Study to Determine the Health System Costs of Using the 21-Gene Assay
Bibliographic record
Abstract
Purpose The 21-gene assay Oncotype Dx (Genomic Health, Redwood City, CA) test is used to aid the decision about chemotherapy in patients with hormone receptor-positive breast cancer who received endocrine therapy. Economic studies to support test adoption used decision-analytic models with assumptions and data derived from disparate sources. The objective was to evaluate whether the 21-gene assay test resulted in an overall cost expense or saving to the health system. Patients and Methods One thousand participants enrolled in a field evaluation study, were linked to population-level health system administrative databases, and were observed for 20 months. The cost for the cohort, which included the cost of the test, subsequent treatments received, and health care encounters, was determined. The cost in the absence of the test was compared with the pretest recommendation about chemotherapy from the field study for a base case and under scenarios that reflected different adjuvant chemotherapy use. Overall health system costs and incremental costs were calculated. Results The 21-gene assay resulted in a net decrease in chemotherapy use of 23%. For the base case incremental analysis, the actual overall health system cost of this cohort, including the cost of 21-gene assay, was $29.2 million compared with $26.2 million in the absence of the test-an increase of $3.1 million. For three of the four scenario analyses, the actual overall cost to the health system exceeded the estimated cost in the absence of the test. Results showed that, when at least half of the population received adjuvant chemotherapy, the cost increased to $30.2 million. Conclusion The use of real-world administrative data showed that, despite lower rates of chemotherapy use, the 21-gene assay test results in an overall incremental cost to the health care system in the short-term under most assumptions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".