Coffee and tea consumption and glioma risk: a meta-analysis of cohort studies
Bibliographic record
Abstract
Background: Research on the association between glioma risk and coffee and tea consumption remains inconclusive. This study seeks to present a meta-analysis of the relationship between coffee and tea intake and glioma risk. Method: Relevant cohort studies that collected coffee and tea exposure prospectively were identified through searches of the PubMed, Embase, and Scopus databases. Eligible studies included those providing adjusted relative risk estimates or hazard ratios (HRs) with 95% confidence intervals (CIs), or data sufficient for such calculations. Study quality was evaluated using the Newcastle-Ottawa Scale, while the GRADE system assessed the quality of evidence. The analysis explored glioma risk concerning the highest versus lowest levels of coffee and tea intake, supplemented by a dose-response evaluation using a one-stage robust error meta-regression model. Results: A total of nine studies, published between 2004 and 2020, were included. In a model comparing the highest and lowest levels of coffee and tea consumption, 3,896 glioma cases were identified among 2,648,468 participants. Correspondingly, the pooled HRs with 95% CIs were 0.98 (0.87-1.09) for coffee and 0.95 (0.86-1.06) for tea, respectively. Furthermore, no evidence of publication bias was detected for either beverage. The dose-response analysis indicated a near "L"-shaped relationship between tea consumption and glioma risk, with the most notable risk reduction observed in individuals consuming more than 2.5 cups of tea per day. However, additional tea intake beyond this threshold did not confer evident risk reduction. According to Grade scoring system, the quality of meta-evidence was classified as "very low" for coffee and "low" for tea. Conclusion: This meta-analysis provides evidence suggesting a potential inverse association between tea consumption and glioma risk, while no such association was observed for coffee consumption. Given that the evidence for coffee was classified as "very low" and for tea as "low," cautious interpretation of the findings is warranted, and further research is needed to validate these results.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.068 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| 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".