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Record W2071777056 · doi:10.1159/000073723

Use of Caffeine-Based Products and Tobacco in Relation to the Consumption of Alcohol

2003· article· en· W2071777056 on OpenAlex
Zalman Amit, Shoshana Weiss, Brian R. Smith, Shimon Markevitch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Addiction Research · 2003
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsConcordia University
Fundersnot available
KeywordsAlcoholCaffeineEnvironmental healthCigarette smokingAlcohol consumptionAlcohol intakeMedicinePsychologyPsychiatryInternal medicineBiology

Abstract

fetched live from OpenAlex

The relations between the intake of alcohol and that of caffeinated beverages, as well as cigarette smoking, was examined in a group of chronic alcoholics in an Israeli treatment center. When data from the total sample was analyzed, relationships between alcohol and caffeinated beverages intake and between alcohol intake and smoking were observed. Caffeine use and smoking were also related. In addition, a subgroup of subjects with a family history of alcoholism revealed correlations between alcohol and caffeine consumption, between alcohol intake and smoking, as well as caffeine use and smoking. Subjects without a family history of alcoholism also showed relationships between alcohol and caffeine use and smoking. However, coffee intake and tobacco use was not related in this subgroup. The relevance of the findings to previous reports concerning alcohol drinking and smoking as well as the intake of coffee appear to be consistent with a notion of interaction between these respective behaviors occurring at a behavioral level rather than a genetic one.

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.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.455
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.247
GPT teacher head0.402
Teacher spread0.155 · 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