Relationship between alcohol consumption and the risks of liver cancer, esophageal cancer, and gastric cancer in China
Bibliographic record
Abstract
OBJECTIVE: : To study the correlation between alcohol consumption and the risks of liver, esophageal squamous cell carcinoma (ESCC), and gastric cancers in China mainland by meta-analysis. METHODS: : We systematically searched electronic databases to identify the case-control studies that reported the association between alcohol consumption and the risks of liver, ESCC, and gastric cancers from January 1, 2010 to April 1, 2020. The Newcastle-Ottawa Scale (NOS) was used to evaluate literature quality, and I2 analyzes were used to evaluate the heterogeneity. RESULTS: : A total of 2855-related studies were retrieved. After conditional screening, we included 26 case-control studies for meta-analysis. Meta-analysis showed that alcohol consumption was associated with increased risks of liver, ESCC, and gastric cancers (total pooled odds ratio [OR], 1.83; 95% confidence interval [CI], 1.58-2.11; liver cancer OR, 1.83; 95% CI, 1.39-2.40; ESCC OR, 2.00; 95% CI, 1.66-2.40; gastric-cancer OR, 1.54; 95% CI, 1.10-2.15). Subgroup analysis results showed that the pooled ORs of volume of alcohol consumed, years of drinking, age of starting drinking, and drinking status were 1.71 (95% CI, 1.36-2.15), 1.65 (95% CI, 1.33-2.06), 1.38 (95% CI, 0.98-1.94), and 2.00 (95% CI, 1.42-2.81), respectively. Regression analysis showed that geographical region was a source of heterogeneity. CONCLUSION: : Alcohol consumption increased the risks of liver cancer, ESCC, and gastric cancers in China. Volume of alcohol consumed, years of drinking, age of starting drinking, and drinking status were all significant factors for these risks.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".