Costs and Benefits of Extended Endocrine Strategies for Premenopausal Breast Cancer
Bibliographic record
Abstract
Background: After completing 5 years of adjuvant tamoxifen, women with estrogen receptor (ER)–positive breast cancer benefit from 5 more years of endocrine therapy, either with tamoxifen or an aromatase inhibitor (AI). For premenopausal women, ovarian ablation (OA) would be required before starting an AI (OA/AI). According to the SOFT/TEXT studies, OA/AI improves 5-year disease-free survival compared with tamoxifen alone, suggesting that OA/AI could be superior to tamoxifen as extended endocrine therapy. The long-term costs and consequences of premature menopause from OA are unknown, but could be estimated through a cost-effectiveness analysis. Methods: A Markov chain Monte Carlo simulation model estimated the costs and benefits of 3 extended endocrine strategies in a hypothetical cohort of premenopausal women with ER-positive early breast cancer: (1) no further treatment; (2) tamoxifen for 5 years (extended tamoxifen); or (3) OA/AI for 5 years. Effectiveness was measured in years of life expectancy gain. Sensitivity analyses accounted for uncertainty surrounding various parameters. Monte Carlo simulation estimated the number of adverse events and deaths from each strategy in the US population over a 40-year period. Results: Extended tamoxifen yielded a higher average life expectancy gain than OA/AI (17.31 vs 17.06 years) at lower average cost ($3,550 vs $14,312). For 18,000 premenopausal ER-positive women, the simulation estimated 13,236, 12,557, and 11,338 deaths with no further treatment, extended tamoxifen, and OA/AI, respectively, but an additional 1,897 deaths from OA, for a total of 13,235 deaths associated with OA/AI. After 24.6 years of follow-up, more women are expected to die from OA/AI than extended tamoxifen. Conclusions: For premenopausal women with ER-positive cancer who have completed adjuvant tamoxifen, another 5 years of tamoxifen is the preferable extended endocrine strategy. The potential long-term health consequences of OA could affect overall survival when it precedes the use of an AI.
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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 | 0.001 |
| 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".