Impact of tea drinking upon tuberculosis: a neglected issue
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is a global public health issue posing serious harm to the human health. Many studies have suggested that smoking and excessive alcohol consumption are risk factors for TB. Laboratory evidence suggests that EGCG in tea leaves can arrest the growth of tubercle bacillus. Can drinking tea lead to decreased susceptibility of TB in humans? METHODS: A total of 574 TB patients and 582 healthy controls were recruited to participate in this case-control study. Self-designed questionnaire was used to collect data. Unconditioned logistic regression analysis was conducted to identify the associations between tea drinking and TB. RESULTS: Tea drinking has a negative association with TB, with OR = 0.583(0.423, 0.804) and P < 0.05. Drinking black tea, oolong and green tea are all negative association with TB, with OR being 0.683(0.517, 0.902), 0.674(0.508, 0.894) and 0.534(0.349, 0.817) respectively and P < 0.05. Trend χ (2) test indicated a decreasing risk for TB with increased tea consumption, with P < 0.05. CONCLUSION: There is a significance negative association between tea drinking and TB. Promoting the consumption of tea as the daily drink among populations, particularly those with high TB risk, may reduce the incidence of TB in the populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".