Alcohol Consumption and Risk of Liver Cirrhosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To systematically summarize the risk relationship between different levels of alcohol consumption and incidence of liver cirrhosis. METHODS: MEDLINE and Embase were searched up to March 6, 2019, to identify case-control and cohort studies with sex-specific results and more than 2 categories of drinking in relation to the incidence of liver cirrhosis. Study characteristics were extracted and random-effects meta-analyses and meta-regressions were conducted. RESULTS: A total of 7 cohort studies and 2 case-control studies met the inclusion criteria, providing data from 2,629,272 participants with 5,505 cases of liver cirrhosis. There was no increased risk for occasional drinkers. Consumption of one drink per day in comparison to long-term abstainers showed an increased risk for liver cirrhosis in women, but not in men. The risk for women was consistently higher compared to men. Drinking ≥5 drinks per day was associated with a substantially increased risk in both women (relative risk [RR] = 12.44, 95% confidence interval [CI]: 6.65-23.27 for 5-6 drinks, and RR = 24.58, 95% CI: 14.77-40.90 for ≥7 drinks) and men (RR = 3.80, 95% CI: 0.85-17.02, and RR = 6.93, 95% CI: 1.07-44.99, respectively). Heterogeneity across studies indicated an additional impact of other risk factors. DISCUSSION: Alcohol is a major risk factor for liver cirrhosis with risk increasing exponentially. Women may be at higher risk compared to men even with little alcohol consumption. More high-quality research is necessary to elucidate the role of other risk factors, such as genetic vulnerability, body weight, metabolic risk factors, and drinking patterns over the life course. High alcohol consumption should be avoided, and people drinking at high levels should receive interventions to reduce their intake.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".