Breast Cancer and Use of Nonsteroidal Anti-inflammatory Drugs: A Meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it