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Record W4307178975 · doi:10.1111/dar.13563

Liver holidays? A meta‐analysis of drinking the same amount of alcohol daily or non‐daily and the risk for cirrhosis

2022· review· en· W4307178975 on OpenAlexaff
Laura Llamosas‐Falcón, Alexander Tran, Huan Jiang, Jürgen Rehm

Bibliographic record

VenueDrug and Alcohol Review · 2022
Typereview
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsMedicineMeta-analysisCirrhosisAbstinenceConfidence intervalRelative riskEnvironmental healthAlcoholAlcohol consumptionInternal medicinePsychiatryBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Several alcohol drinking guidelines indicate that daily alcohol consumption should be avoided because of its negative impact on the liver and to avoid the development of alcohol use disorders. Evidence that supports this recommendation is scarce. Our aim was to compare daily versus non-daily drinking and its association with liver cirrhosis. METHODS: We conducted a review using PubMed/Medline and Embase as databases, selecting longitudinal or case control studies. A random effects meta-analysis was conducted. RESULTS: Five mainly large-scale studies were retrieved. Daily drinking was associated with a significant increase in risk of liver cirrhosis compared to non-daily drinking, with a pooled relative risks of 1.71 (95% confidence interval 1.23-2.23) for men and 1.56 (95% confidence interval 1.39-1.74) for women. DISCUSSION AND CONCLUSIONS: The consistent exposure to acetaldehyde and other toxins for daily drinkers may explain our findings. There should be days of abstinence to allow the liver to recover, especially for heavier drinkers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.220
GPT teacher head0.429
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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