Coffee consumption and risk of endometrial cancer: Findings from a large up‐to‐date meta‐analysis
Bibliographic record
Abstract
Several epidemiological studies have examined the association between coffee drinking and risk of endometrial cancer. To provide a quantitative assessment of this association, we conducted a meta-analysis of observational studies published up to October 2011 through a search of MEDLINE and EMBASE databases and the reference lists of retrieved article. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and generalized least square trend estimation was used to assess dose-response relationships. A total of 16 studies (10 case-control and six cohort studies) on coffee intake with 6,628 endometrial cancer cases were included in the meta-analysis. The pooled RR of endometrial cancer for the highest versus lowest categories of coffee intake was 0.71 (95% CI: 0.62-0.81; p for heterogeneity = 0.13). By study design, the pooled RRs were 0.69 (95% CI: 0.55-0.87) for case-control studies and 0.70 (95% CI: 0.61-0.80) for cohort studies. By geographic region, the inverse association was stronger for three Japanese studies (pooled RR = 0.40; 95% CI: 0.25-0.63) than five studies from USA/Canada (pooled RR = 0.69; 95% CI: 0.60-0.79) or eight studies from Europe (pooled RR = 0.79; 95% CI: 0.63-0.99). An increment of one cup per day of coffee intake conferred a pooled RR of 0.92 (95% CI: 0.90-0.95). In conclusion, our findings suggest that increased coffee intake is associated with a reduced risk of endometrial cancer, consistently observed for cohort and case-control studies. More large studies are needed to determine subgroups to obtain more benefits from coffee drinking in relation to endometrial cancer risk.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".