A Population-Based Case-Control Study of Lung Cancer and Green Tea Consumption among Women Living in Shanghai, China
Bibliographic record
Abstract
Epidemiologic evidence regarding the association between the consumption of green tea and lung cancer is limited and inconclusive, although experimental studies have shown consistently that tea preparations and tea polyphenols may inhibit the induction of a variety of cancers, including lung cancer. In this population-based case-control study, we examined the association between past consumption of green tea and the risk of lung cancer. We identified 649 incident cases of primary lung cancer among women diagnosed from February 1992 through January 1994 using the population-based Shanghai Cancer Registry. We randomly selected a control group of 675 women from the Shanghai Residential Registry, frequency-matched to the expected age distribution of the cases. Green tea consumption was ascertained through face-to-face interviews. We estimated adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) using unconditional logistic regression. Among nonsmoking women, consumption of green tea was associated with a reduced risk of lung cancer (OR = 0.65; 95% CI = 0.45-0.93), and the risks decreased with increasing consumption. We found little association, however, among women who smoked (OR = 0.94; 95% CI = 0.40-2.22). The inconsistency in the association between drinking tea and the risk of lung cancer reported in previous studies may in part be due to inadequate control of confounding of active smoking.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".