Association between frequent use of nonsteroidal anti-inflammatory drugs and breast cancer
Bibliographic record
Abstract
BACKGROUND: Eighty percent of all breast cancers and almost 90% of breast cancer deaths occur among post-menopausal women. We used a nested case control design to examine the association between nonsteroidal anti-inflammatory drug (NSAID) use and breast cancer occurrence among women over 65 years of age. The cyclooxygenase (COX)-2 enzyme is expressed more in breast cancers than in normal breast tissue. COX-2 inhibition may have a role in breast cancer prevention. METHODS: In the Canadian province of Quebec, physician services are covered through a governmental insurance plan. Medication costs are covered for those > or = 65 years of age and a publicly funded screening program for breast cancer targets all women 50 years of age or older. We obtained encrypted data from these insurance databases on all women > or = 65 years of age who filled a prescription for COX-2 inhibitors, non-selective NSAIDs (ns-NSAIDs), aspirin, or acetaminophen between January 1998 and December 2002. Cases were defined as those women who have undergone mammography between April 2001 and June 2002 and had a diagnosis of breast cancer within six months following mammography. Controls included those who have undergone mammography between April 2001 and June 2002 without a diagnosis of any cancer during the six months following mammography. The exposure of interest, frequent NSAID use, was defined as use of ns-NSAIDs and/or COX-2 inhibitors for > or = 90 days during the year prior to mammography. Frequent use served as a convenient proxy for long term chronic use. RESULTS: We identified 1,090 cases and 44,990 controls. Cases were older and more likely to have breast cancer risk factors. Logistic regression models adjusting for potential confounders showed that frequent use of ns-NSAIDs and/or COX-2 inhibitors was associated with a lower risk of breast cancer (OR: 0.75, 95% confidence interval 0.64-0.89). Results were similar for COX-2 inhibitors (0.81, 0.68-0.97) and ns-NSAIDs (0.65, 0.43-0.99), when assessed separately. Frequent use of aspirin at doses > 100 mg/day in the year prior to mammography was also associated with a lower risk of breast cancer (0.75, 0.64-0.89). However, use of aspirin at doses < or = 100 mg/day did not have any association with breast cancer (0.91, 0.71-1.16). CONCLUSION: Women who use NSAIDs or doses of aspirin > 100 mg frequently may have a lower risk of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".