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Record W4409040486 · doi:10.1111/add.70037

Diagnostic validity of drinking behaviour for identifying alcohol use disorder: Findings from a representative sample of community adults and an inpatient clinical sample

2025· article· en· W4409040486 on OpenAlexafffundabout
Molly L. Scarfe, Andriy V. Samokhvalov, Yelena Chorny, Onawa LaBelle, Brian Rush, Mary Jean Costello, James MacKillop

Bibliographic record

VenueAddiction · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of WindsorCentre for Addiction and Mental HealthHomewood Research InstituteUniversity of TorontoMcMaster UniversityPublic Health OntarioSt. Joseph’s Healthcare Hamilton
FundersCanada Research Chairs
KeywordsAlcohol use disorderMedicineReceiver operating characteristicSample (material)ChecklistPredictive valuePredictive validityPsychologyAlcoholClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Alcohol consumption is an inherent feature of alcohol use disorder (AUD), and drinking patterns may be diagnostically informative. This study had three aims: (1) to examine the classification accuracy of several individually analysed drinking behavior measures in a large sample of US community adults; (2) to extend the findings to an adult clinical sample; and (3) to examine potential sex differences. DESIGN: In cross-sectional epidemiological and clinical datasets, receiver operating characteristic (ROC) curves were used to evaluate diagnostic classification using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). SETTING AND PARTICIPANTS: Two samples were examined: a large random sample of US community adults who reported past-year drinking (n = 25 773, AUD = 20%) and a clinical sample from a Canadian inpatient addiction treatment centre (n = 1341, AUD = 82%). MEASUREMENTS: Classifiers included measures of quantity/frequency (e.g. drinks/drinking day, largest drinks/drinking day, number of drinking days and heavy drinking frequency). The clinical criterion (reference standard) was AUD diagnostic status per structured clinical interview (community sample) or a symptom checklist (clinical sample). FINDINGS: All drinking indicators were statistically significant classifiers of AUD (AUCs = 0.60-0.92, Ps<0.0001). Heavy drinking frequency indicators performed optimally in both the community (AUCs = 0.78-0.87; accuracy = 0.72-0.80) and clinical (AUCs = 0.85-0.92; accuracy = 0.77-0.89) samples. Collectively, the most discriminating drinking behaviours were number of heavy drinking episodes and frequency of exceeding drinking low-risk guidelines. No substantive sex differences were observed across drinking metrics. CONCLUSIONS: Quantitative drinking indices appear to perform well at classifying alcohol use disorder (AUD) in both a large community adult and inpatient sample, robustly identifying AUD at rates much better than chance and above accepted clinical classification benchmarks, with limited differences by sex. These findings broadly support the potential clinical utility of quantitative drinking indicators in routine patient assessment.

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)0.004
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 score0.009
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 head0.137
GPT teacher head0.416
Teacher spread0.279 · 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 routes3
Has abstractyes

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