Analgesic Use and Ovarian Cancer Risk: An Analysis in the Ovarian Cancer Cohort Consortium
Bibliographic record
Abstract
BACKGROUND: Aspirin use is associated with reduced risk of several cancers. A pooled analysis of 12 case-control studies showed a 10% decrease in ovarian cancer risk with regular aspirin use, which was stronger for daily and low-dose users. To prospectively investigate associations of analgesic use with ovarian cancer, we analyzed data from 13 studies in the Ovarian Cancer Cohort Consortium (OC3). METHODS: The current study included 758 829 women who at study enrollment self-reported analgesic use, among whom 3514 developed ovarian cancer. Using Cox regression, we assessed associations between frequent medication use and risk of ovarian cancer. Dose and duration were also evaluated. All statistical tests were two-sided. RESULTS: Women who used aspirin almost daily (≥6 days/wk) vs infrequent/nonuse experienced a 10% reduction in ovarian cancer risk (rate ratio [RR] = 0.90, 95% confidence interval [CI] = 0.82 to 1.00, P = .05). Frequent use (≥4 days/wk) of aspirin (RR = 0.95, 95% CI = 0.88 to 1.03), nonaspirin nonsteroidal anti-inflammatory drugs (NSAIDs; RR = 1.00, 95% CI = 0.90 to 1.11), or acetaminophen (RR = 1.05, 95% CI = 0.88 to 1.24) was not associated with risk. Daily acetaminophen use (RR = 1.28, 95% CI = 1.00 to 1.65, P = .05) was associated with elevated ovarian cancer risk. Risk estimates for frequent, long-term (10+ years) use of aspirin (RR = 1.15, 95% CI = 0.98 to 1.34) or nonaspirin NSAIDs (RR = 1.19, 95% CI = 0.84 to 1.68) were modestly elevated, although not statistically significantly so. CONCLUSIONS: This large, prospective analysis suggests that women who use aspirin daily have a slightly lower risk of developing ovarian cancer (∼10% lower than infrequent/nonuse)-similar to the risk reduction observed in case-control analyses. The observed potential elevated risks for 10+ years of frequent aspirin and NSAID use require further study but could be due to confounding by medical indications for use or variation in drug dosing.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".