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Beverage‐specific alcohol consumption and cirrhosis mortality in a group of English‐speaking beer‐drinking countries

2000· article· en· W2030107685 on OpenAlexaboutno aff
William C. Kerr, Kaye Middleton Fillmore, Paul Marvy

Bibliographic record

VenueAddiction · 2000
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsPer capitaCirrhosisMedicineConsumption (sociology)Alcohol consumptionAlcoholAlcoholic liver diseaseEnvironmental healthWineDemographyMortality ratePopulationSurgeryInternal medicineFood scienceBiology

Abstract

fetched live from OpenAlex

AIMS: To compare beverage-specific per capita consumption and total alcohol consumption's associations with cirrhosis mortality rates in multiple countries. DESIGN: Pooled cross-sectional time-series analysis. SETTING: Australia, Canada, New Zealand, the United Kingdom and the United States during the years 1953-1993. MEASUREMENTS: National level data on per capita total alcohol, beer, wine and spirits consumption and standardized all-cause cirrhosis mortality rates. FINDINGS: Significant associations with cirrhosis mortality are found for both total ethanol and spirits. Spirits consumption is found to make up the majority of the effect of alcoholic beverage consumption on cirrhosis mortality and the model including only spirits is found to fit the data at least as well as the model including only total ethanol consumption. The lag relationship between all alcohol types and cirrhosis is found to be short with only present and 1 year's lagged consumption having significant associations. CONCLUSIONS: Spirits consumption rather than beer or wine is associated with cirrhosis mortality in this group of primarily beer-drinking countries. This finding offers important clues to understanding the drinking behaviors associated with cirrhosis mortality on the individual level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.061
GPT teacher head0.335
Teacher spread0.274 · 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 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

Citations111
Published2000
Admission routes1
Has abstractyes

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