Raloxifene and Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of raloxifene on colorectal cancer (CRC) risk. METHODS: We analyzed data from the Multiple Outcomes of Raloxifene Evaluation (MORE) trial, a randomized, blinded clinical trial designed to determine the effect of raloxifene on vertebral fracture risk. In this trial, 7705 women received either raloxifene or placebo and were followed for an average of 3.4 years. CRC cases were classified as definite (pathology available), probable (imaging or colonoscopic diagnosis), or possible (self-report). Relative hazard for CRC was calculated using multivariate Cox proportional hazards models. RESULTS: Fifty cases of definite or probable CRC were diagnosed; 40 were definite and 10 probable. Twenty-nine cases occurred among the 5129 women in the raloxifene group, and 21 occurred among the 2576 women in the placebo group (p = 0.15). The relative hazard for CRC for women treated with raloxifene was 0.78 (95% CI 0.43, 1.43, p = 0.43). Restricting the analysis to definite CRC, the relative hazard was 0.77 (95% CI 0.39, 1.5, p = 0.45). CONCLUSIONS: Although the MORE trial was large, the number of CRC cases was too small to provide definitive evidence concerning the effect of raloxifene on CRC risk. There does not appear to be a substantial increased risk of CRC with raloxifene use. Studies including larger numbers of women and women at risk for CRC should further investigate the effect of raloxifene on CRC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".