Nonsteroidal Antiinflammatory Drug Use and Breast Cancer Risk: Subgroup Findings
Bibliographic record
Abstract
Nonsteroidal antiinflammatory drugs (NSAIDs) may play a role in breast cancer prevention; however, breast cancer subtypes and lifestyle/host factors may influence their impact. During 1996-1998 in Canada, the authors examined the association between regular NSAID use (defined as daily use for at least 2 months) and breast cancer risk by estrogen receptor (ER) and progesterone receptor (PR) status, cigarette smoking exposure, and history of arthritis. Breast cancer cases (n = 3,125, including 1,600 ER+PR+ and 591 ER-PR-) and an age-matched, random sample of controls (n = 3,062) completed a general risk factor questionnaire, including detailed questions on prescription and nonprescription NSAID use. NSAID use was associated with reduced risk of breast cancer (odds ratio = 0.76, 95% confidence interval: 0.66, 0.88). The association was not significantly different for ER+PR+ (odds ratio = 0.71, 95% confidence interval: 0.60, 0.84) and ER-PR- cancers (odds ratio = 0.80, 95% confidence interval: 0.62, 1.03) (p(heterogeneity) = 0.66). The magnitude of the NSAID inverse association was similar for women with and without arthritis and across smoking strata (risk estimates ranged from 0.74 to 0.84). Breast cancer risk tended to decrease with increasing duration of NSAID use and was generally lowest for >or=7 years of use, and both acetylsalicylic acid and non-acetylsalicylic acid use were associated with reduced risks.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".