First do no harm: extending the debate on the provision of preventive tamoxifen
Bibliographic record
Abstract
The Breast Cancer Prevention Trial (BCPT-P-1) demonstrated that tamoxifen could reduce the risk of invasive breast cancer in high-risk women by 49%, but that it could also increase the risk of endometrial cancer, vascular events and cataracts. This paper provides an estimate of the net health impacts of tamoxifen administration on high-risk Canadian women with no prior history of breast cancer. The results of the BCPT-P-1 were incorporated into the breast cancer and other modules of Statistics Canada's microsimulation POpulation HEalth Model (POHEM). While the main intervention scenario conformed as closely as possible to the eligibility criteria for tamoxifen in the BCPT-P-1 protocol, 3 additional scenarios were simulated. Predicted absolute risks of breast cancer at 5 years of 1.66%, 3.32% and 4.15% were calculated for women 35 to 70 years of age. When the BCPT-P-1 results were incorporated into the simulation model, the analysis suggests no increase in life expectancy in this risk group. Tamoxifen appeared to be beneficial for women with a 5-year predicted risk of 3.32% or greater. The results of these simulations are particularly sensitive to the reduction in mortality observed in the BCPT-P-1, as well as being sensitive to other characteristics of the simulation model. Overall, the analysis raises questions about the use of tamoxifen in otherwise healthy women at high risk of breast cancer.
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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 | 0.038 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".