Cost analysis of using Magee scores as a surrogate of Oncotype DX for adjuvant treatment decisions in women with early breast cancer
Bibliographic record
Abstract
Breast cancer is the most common cancer in women worldwide. Most current guidelines recommend using multigene profiling assays to aid the decision on the addition of chemotherapy to adjuvant hormone therapy for women who present with early-stage, hormone receptor-positive, HER2-negative disease. One of these assays is the Oncotype DX, which predicts the disease recurrence risk and adjuvant chemotherapy benefits. Given its high cost, there is an economic incentive to evaluate its surrogates, such as the Magee equations. We assessed health system costs associated with the use of the Magee scores. A probabilistic decision tree was used to calculate the difference in mean health system costs based on data obtained from a randomized trial and the published literature. Costs were calculated from a perspective of Canada's publicly funded health care system. A series of sensitivity analysis was conducted to assess the robustness of the study findings. The Magee equations were associated with a total cost savings of C$100 per patient (95% CI, -C$3068 to C$5022) compared with standard of care. The difference in costs was highly sensitive to the extent that the Magee scores could reduce the frequency of adjuvant chemotherapy and Oncotype DX requests.
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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 | 0.037 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".