The Relation between Caffeine Consumption and Endometriosis: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
While the contributing factors leading to endometriosis remain unclear, its clinical heterogeneity suggests a multifactorial causal background. Amongst others, caffeine has been studied extensively during the last decade as a putative contributing factor. In this systematic review and meta-analysis, we provide an overview/critical appraisal of studies that report on the association between caffeine consumption and the presence of endometriosis. In our search strategy, we screened PubMed and Scopus for human studies examining the above association. The main outcome was the relative risk of endometriosis in caffeine users versus women consuming little or no caffeine (<100 mg/day). Subgroup analyses were conducted for different levels of caffeine intake: high (>300 mg/day) or moderate (100–300 mg/day). Ten studies were included in the meta-analysis (five cohort and five case-control studies). No statistically significant association was observed between overall caffeine consumption and risk for endometriosis (RR 1.12, 95% confidence interval (CI) 0.97–1.28, I2 = 70%) when compared to little or no (<100 mg/day) caffeine intake. When stratified according to level of consumption, high intake was associated with increased risk of endometriosis (RR 1.30, 95%CI 1.04–1.63, I2 = 56%), whereas moderate intake did not reach nominal statistical significance (RR 1.18, 95%CI 0.99–1.40, I2 = 37%). In conclusion, caffeine consumption does not appear to be associated with increased risk for endometriosis. However, further research is needed to elucidate the potential dose-dependent link between caffeine and endometriosis or the probable role of caffeine intake as a measurement of other unidentified biases.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".