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Record W4410454068 · doi:10.1007/s11901-025-00688-5

Public Health Policies and Strategies to Prevent Alcohol-Related Morbidity and Mortality

2025· article· en· W4410454068 on OpenAlexaff
Roba El Zibaoui, Luis Antonio Díaz, Francisco Idalsoaga, Juan Pablo Arab

Bibliographic record

VenueCurrent Hepatology Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPublic healthEnvironmental healthMedicineAlcoholPublic health policyIntensive care medicineHealth policyNursingBiology

Abstract

fetched live from OpenAlex

Abstract Purpose of Review Alcohol consumption significantly contributes to global morbidity and mortality, particularly in individuals with alcohol-associated liver disease (ALD). This review aims to evaluate the effectiveness of public health policies in reducing alcohol-related harm, focusing on SAFER initiatives and World Health Organization (WHO) “best buys.” Recent Findings Key strategies to reduce the affordability of alcohol, such as alcohol taxation, minimum unit pricing, and legislation, have proven effective in reducing alcohol consumption and ALD-related deaths. However, their success varies across different regions and populations. Innovative approaches to further mitigate alcohol-related harm are also explored. Summary The review highlights the need for more robust, evidence-based public health policies to address alcohol-related diseases. A comprehensive, focused approach is essential to mitigate the global alcohol epidemic and its consequences, with an emphasis on policy refinement and greater understanding of alcohol-related harm.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.225
GPT teacher head0.465
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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