Impact and Cost-Effectiveness of Oncotype DX for Guiding Adjuvant Chemotherapy Decisions in Early Breast Cancer.
Bibliographic record
Abstract
INTRODUCTION: Oncotype DX is approved in multiple countries but its cost-effectiveness is a matter of considerable health debate. Lebanon is high-middle income country according to the World Bank classification however it is facing a mounting financial and health care burden from cancer. Therefore, we conducted a costeffectiveness analysis of Oncotype DX based Lebanese on real-life data. METHODS: We updated a Canadian cost-effectiveness model of Oncotype DX by incorporating Lebanese data. The patient population was a real-life cohort of 82 women diagnosed with hormone receptor - positive and HER2 - negative early breast cancer. RESULTS: Overall, providing Oncotype DX to only intermediate Adjuvant! Online risk patients costs an additional $83 CAD (93,883 LBP) per additional QALY. From this point, extending provision to also cover high Adjuvant! Online risk patients costs an additional $736 CAD (831,578 LBP) per additional QALY. From this point, extending provision further to also cover low Adjuvant! Online risk patients (such that Oncotype DX is provided to all patients) costs an additional $14,562 CAD (16.46m LBP) per additional QALY. Given that most women in our population-based sample were classified as intermediate Adjuvant! Online risk patients, our study focused on this subset in the second analysis. Providing Oncotype DX to intermediate Adjuvant! Online risk patients has a relatively small additional cost compared to not providing Oncotype DX, and results in a relatively large QALY gain. The incremental cost per QALY is $2,022 CAD (2.29m LBP), implying that Oncotype DX is cost-effective for intermediate Adjuvant! Online risk patients if the willingness-to-pay for a QALY is greater than 2.29m LBP. CONCLUSION: As one of the few economic evaluations to date conducted using Lebanese data, this evaluation provides information to decision makers regarding the cost-effectiveness of providing Oncotype DX to Lebanese patients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".