Alcohol as a risk factor for liver cirrhosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Alcohol is an established risk factor for liver cirrhosis. It remains unclear, however, whether this relationship follows a continuous dose-response pattern or has a threshold. Also, the influences of sex and end-point (i.e. mortality vs. morbidity) on the association are not known. To address these questions and to provide a quantitative assessment of the association between alcohol intake and risk of liver cirrhosis, we conducted a systematic review and meta-analysis of cohort and case-control studies. DESIGN AND METHODS: Studies were identified by a literature search of Ovid MEDLINE, EMBASE, Web of Science, CINAHL, PsychINFO, ETOH and Google Scholar from January 1980 to January 2008 and by searching the references of retrieved articles. Studies were included if quantifiable information on risk and related confidence intervals with respect to at least three different levels of average alcohol intake were reported. Both categorical and continuous meta-analytic techniques were used to model the dose-response relationship. RESULTS: Seventeen studies met the inclusion criteria. We found some indications for threshold effects. Alcohol consumption had a significantly larger impact on mortality of liver cirrhosis compared with morbidity. Also, the same amount of average consumption was related to a higher risk of liver cirrhosis in women than in men. DISCUSSION AND CONCLUSIONS: Overall, end-point was an important source of heterogeneity among study results. This result has important implications not only for studies in which the burden of disease attributable to alcohol consumption is estimated, but also for prevention.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".