Preventive effects of tea and tea catechins against influenza and acute upper respiratory tract infections: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Gargling with tea has protective effects against influenza infection and upper respiratory tract infection (URTI). To evaluate if tea and tea catechin consumption has the same protective effects as gargling with tea, we performed a systematic review and meta-analysis. METHODS: We performed a comprehensive literature search using the PubMed, Cochrane Library, Web of Science, and Ichu-shi Web databases. The search provided six randomized controlled trials (RCTs) and four prospective cohort studies (n = 3748). The quality of each trial or study was evaluated according to the Cochrane risk-of-bias tool or Newcastle-Ottawa Scale. We collected data from publications meeting the search criteria and conducted a meta-analysis of the effect of tea gargling and tea catechin consumption for preventing URTI using a random effects model. RESULTS: Tea gargling and tea catechin consumption had significant preventive effects against URTI (risk ratio [RR] = 0.74, 95% confidence interval [CI] 0.64-0.87). In sub-analyses, a significant preventive effect was observed by study type (prospective cohort study: RR = 0.67, 95% CI 0.50-0.91; RCT: RR = 0.79, 95% CI 0.66-0.94) and disease type (influenza: RR = 0.69, 95% CI 0.58-0.84; acute URTI: RR = 0.78, 95% CI 0.62-0.98). Both gargling with tea and consuming tea catechins effectively protected against URTI (tea and tea catechins consumption: RR = 0.68, 95% CI 0.52-0.87; tea gargling: RR = 0.83, 95% CI 0.72-0.96). CONCLUSION: Our findings suggest that tea gargling and tea catechin consumption may have preventive effects against influenza infection and URTI. The potential effectiveness of these actions as non-pharmaceutical interventions, however, requires further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".