Chemoprevention: Drug pricing and mortality
Bibliographic record
Abstract
BACKGROUND: Tamoxifen is a prototypic cancer chemopreventive agent, yet clinical trials have not evaluated its effect on mortality or the impact of drug pricing on its cost-effectiveness. METHODS: A state-transition Markov model for a hypothetical cohort of women age 50 years was used to evaluate the effects of tamoxifen on mortality and tamoxifen price on cost-effectiveness. Incidence and mortality rates for breast and endometrial cancers were derived from Surveillance, Epidemiology and End Results statistics, and noncancer outcomes were obtained from published studies. Relative risks of outcomes were derived from the National Surgical Adjuvant Breast and Bowel Project P-1 trial. Costs were based on Medicare reimbursements. RESULTS: Projected overall mortality for women at 1.67% 5-year breast cancer risk showed little difference with or without tamoxifen, resulting in a cost-effectiveness ratio of $1,335,690 per life-year saved as a result of tamoxifen use. Adjusting for the differential impact of estrogen receptor-negative cancers, tamoxifen increased mortality for women with a uterus until the 5-year breast cancer risk reached > or =2.1%. Assigning the Canadian price for tamoxifen dramatically reduced the incremental cost (to $123,780 per life-year saved). At that price, the use of tamoxifen was less costly and more effective for women with 5-year breast cancer risks >4%. CONCLUSIONS: Tamoxifen may increase mortality in women at the lower end of the "high-risk" range for breast cancer. If prices in the U.S. approximated Canadian prices, then tamoxifen use for breast cancer risk reduction in women with a 5-year risk >3% could be a reasonable strategy to reduce the incidence of breast cancer. Because they are used by many unaffected individuals, the price of chemopreventive agents has a major influence on their cost-effectiveness.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".