Nonsteroidal anti-inflammatory drug use and breast cancer risk: a Danish cohort study
Bibliographic record
Abstract
Epidemiologic studies investigating the effects of nonsteroidal anti-inflammatory drugs (NSAIDs) on breast cancer have yielded conflicting results. We examined the association between use of aspirin and nonaspirin NSAIDs and breast cancer risk among 28 695 women in the Danish Diet, Cancer and Health cohort. Information on NSAID and paracetamol use was obtained from a self-administered questionnaire completed at baseline (1993-1997) and updated through 2003 using a nationwide prescription database. Detailed information on breast cancer incidence and tumour characteristics was obtained from nationwide health registers. Cox proportional hazards regression was used to compute incidence rate ratios (RRs) and 95% confidence intervals (CIs). We identified 847 breast cancer cases over an average follow-up period of 7.5 years. Any NSAID use at baseline was associated with an increased incidence of breast cancer compared with nonuse (RR, 1.27; 95% CI, 1.10-1.45). A similar result was observed for any NSAID use in a combined analysis of baseline and prescription data (1.34; 95% CI, 1.15-1.56). Aspirin-only users experienced a slightly higher breast cancer incidence (RR, 1.38; 95% CI, 1.12-1.69) than exclusive users of nonaspirin NSAIDs (RR, 1.25; 95% CI, 1.04-1.49). Introduction of a lag time of 1 year provided similar results. We found no clear differences in risk estimates with frequency, recency or duration of NSAID use, or by hormone receptor status of the breast tumours. Paracetamol use was unrelated to breast cancer incidence. The increased breast cancer incidence among NSAID users may reflect a noncausal association, but our study provides no evidence of a chemopreventive effect of NSAIDs against breast cancer over the durations studied.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".