MétaCan
Menu
Back to cohort
Record W2560733466 · doi:10.1002/hup.2565

Association between overall rate of change in rising breath alcohol concentration and the magnitude of acute tolerance of subjective intoxication via the Mellanby method

2016· article· en· W2560733466 on OpenAlexaff
David H. Morris, Michael Amlung, Chia‐Lin Tsai, Denis M. McCarthy

Bibliographic record

VenueHuman Psychopharmacology Clinical and Experimental · 2016
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsAssociation (psychology)AlcoholAlcohol intoxicationPsychologyMedicineChemistryEnvironmental healthPoison controlInjury preventionPsychotherapistBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: The magnitude of acute tolerance is a strong predictor of the development of longer-term chronic tolerance and plays a decisive role in risky decisions (e.g., driving after drinking). Therefore, it is important to identify factors that increase the magnitude of this adaptive process. This study explored whether acute tolerance magnitude varied as a function of the overall rate of increase in breath alcohol concentration (BrAC). METHODS: Twenty-nine young adult social drinkers (M age = 22.55, SD = 3.10; 62.1% women) consumed a moderate dose of alcohol (men: 0.86 g/kg, women: 0.75 g/kg) in a controlled laboratory setting. Subjective intoxication was assessed at matched BrACs (~0.060 g/dl) on each limb of the BrAC curve. RESULTS: = .29). CONCLUSIONS: These results present some of the first evidence that faster increases in BrAC may be associated with greater acute tolerance, as indicated by greater reduction in subjective intoxication across the limbs of the BrAC curve. This greater reduction may, in turn, promote heavier drinking and/or engagement in behaviors for which one is unfit (e.g., driving after drinking).

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 score0.024
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.076
GPT teacher head0.457
Teacher spread0.381 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueHuman Psychopharmacology Clinical and ExperimentalSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207