Breast Cancer and Use of Nonsteroidal Anti-inflammatory Drugs: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Breast cancer is one of the leading causes of mortality among women. The use of nonsteroidal anti-inflammatory drugs (NSAIDs) may be associated with reduced risk for breast cancer, but results from these studies of the association have been inconsistent. METHODS: Studies that examined the association between risk of breast cancer and use of NSAIDs, including aspirin and ibuprofen, that were published between January 1, 1966, and July 1, 2008, were identified using Medline, EMBASE, and other databases. We performed meta-analysis by pooling studies according to the inverse of their variances and performed separate analyses of studies pooled according to aspirin use and ibuprofen use. We evaluated publication bias and study quality. RESULTS: A total of 38 studies (16 case-control studies, 18 cohort studies, 3 case-control studies nested in well-defined cohorts, and 1 clinical trial) that included 2 788 715 subjects were identified. The results of these studies suggest that overall, NSAID use was associated with reduced risk for breast cancer (relative risk [RR] = 0.88, 95% confidence interval [CI] = 0.84 to 0.93). Specific analyses for aspirin (RR = 0.87, 95% CI = 0.82 to 0.92) and ibuprofen (RR = 0.79, 95% CI = 0.64 to 0.97) yielded similar results. CONCLUSIONS: This meta-analysis provides evidence that NSAID use is associated with reduced risk for breast cancer. Future research should include careful evaluation of the biologic mechanisms involved in the relationship between NSAIDs and 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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.063 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".