Cost–Utility of Adjuvant Zoledronic Acid in Patients with Breast Cancer and Low Estrogen Levels
Bibliographic record
Abstract
BACKGROUND: Adjuvant zoledronic acid (za) appears to improve disease-free survival (dfs) in women with early-stage breast cancer and low levels of estrogen (lle) because of induced or natural menopause. Characterizing the cost-utility (cu) of this therapy could help to determine its role in clinical practice. METHODS: Using the perspective of the Canadian health care system, we examined the cu of adjuvant endocrine therapy with or without za in women with early-stage endocrine-sensitive breast cancer and lle. A Markov model was used to compute the cumulative costs in Canadian dollars and the quality-adjusted life-years (qalys) gained from each adjuvant strategy, discounted at a rate of 5% annually. The model incorporated the dfs and fracture benefits of adjuvant za. Probabilistic and one-way sensitivity analyses were conducted to examine key model parameters. RESULTS: Compared with a no-za strategy, adjuvant za in the induced and natural menopause groups was associated with, respectively, $7,825 and $7,789 in incremental costs and 0.46 and 0.34 in qaly gains for cu ratios of $17,007 and $23,093 per qaly gained. In one-way sensitivity analyses, the results were most sensitive to changes in the za dfs benefit. Probabilistic sensitivity analysis suggested a 100% probability of adjuvant za being a cost-effective strategy at a threshold of $100,000 per qaly gained. CONCLUSIONS: Based on available data, adjuvant za appears to be a cost-effective strategy in women with endocrine-sensitive breast cancer and lle, having cu ratios well below accepted thresholds.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".