Is temperature an effect modifier of the association between green tea intake and gastric cancer risk?
Bibliographic record
Abstract
We considered the relationship between green tea and gastric cancer risk in Harbin, Heilongjiang province, Northeast China, an area with high baseline risk of stomach cancer. We used data from a case-control study conducted from 1987 to 1989 among 266 incident cases of stomach cancer and 533 controls admitted to the same hospitals as cases, with non-neoplastic and non-gastric diseases. No association emerged when tea consumption alone was considered: the odds ratio (OR) for green tea consumption was 0.87 (95% CI: 0.60-1.25) for green tea intake > or = 750 g/year versus no intake and 0.99 (95% CI: 0.97-1.02) for an increment of 500 g of tea per year. When tea consumption was classified according to the temperature, however, the OR was 0.19 (95% CI: 0.07-0.49) for lukewarm tea intake > or = 750 g/year and 1.27 (95% CI: 0.85-1.90) for hot tea intake (P value for interaction <0.001) as compared with non-drinkers. The corresponding ORs for an increment of 500 g of tea per year were 0.61 (95% CI: 0.45-0.82) and 1.03 (95% CI: 0.99-1.07) for lukewarm and hot tea, respectively (P value for interaction <0.001). We found an inverse relationship between green tea drinking and gastric cancer risk limited to the intake of lukewarm tea.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".