Association of Lung Cancer and Tea-Drinking Habits of Different Subgroup Populations: Meta-Analysis of Case-Control Studies and Cohort Studies
Bibliographic record
Abstract
Background: We aimed to investigate the association between lung cancer and tea-drinking habits of different subgroup populations. Methods: Systematic search of the PubMed, Web of Science, China National Knowledge Infrastructure (CNKI) and Sinomed databases from database construction until January 2017 for English and Chinese language articles on association of lung cancer and tea drinking. Meta-analysis was used to calculate the combined odds ratio (OR) value and its 95% confidence interval (95% CI). The Newcastle-Ottawa scale was used to evaluate the quality of the studies and Q-test and I2 was used for heterogeneity testing. Results: Forty two papers were included, 30 case-control studies included 14578 lung cancer patients and 180574 controls, 12 cohort studies included 543825 subjects, of which the outcome was 5085 with lung cancer. Tea drinkers were found to have a decreased OR of lung cancer compared with non-tea drinkers (OR 0. 80, 95% CI: 0. 73, 0. 87). Consumption of green, black or unspecified tea has a protective effect compared with not drinking tea at all. Increased intake of green tea to 7. 5 g per day can further reduce the OR of lung cancer (OR 0. 69, 95% CI: 0. 48-0. 98). Tea consumption had a protective effect against lung cancer in non-smokers, Further analysis found that drinking of one or more cups of tea a day has a protective effect on smokers (OR 0. 79, 95%CI: 0. 64–0. 96). Conclusion: Tea drinking could be a protective factor in lung cancer.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.008 | 0.009 |
| 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".