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Record W4402554514 · doi:10.1101/2024.09.14.24313683

Diagnostic Validity of Drinking Behaviour for Identifying Alcohol Use Disorder: Findings from a Nationally Representative Sample of Community Adults and an Inpatient Clinical Sample

2024· preprint· en· W4402554514 on OpenAlexaffabout
Molly L Garber, Yelena Chorny, Onawa LaBelle, Brian Rush, Mary Jean Costello, James MacKillop

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of WindsorCentre for Addiction and Mental HealthHomewood Research InstituteUniversity of TorontoMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsSample (material)Alcohol use disorderPsychologyClinical psychologyPsychiatryEnvironmental healthMedicineAlcohol

Abstract

fetched live from OpenAlex

ABSTRACT Background and Aims Alcohol consumption is an inherent feature of alcohol use disorder (AUD), and drinking characteristics may be diagnostically informative. This study had three aims: (1) to examine the classification accuracy of several drinking quantity/frequency indicators in a large representative sample of U.S. community adults; (2) to extend the findings to a clinical sample of adults; and (3) to examine potential sex differences. Design This retrospective study utilized receiver operating characteristic (ROC) curves to evaluate area under the curve (AUC). Optimal cut-offs were identified using the Youden Index. Diagnostic validity was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Measurements Index tests included measures of quantity/frequency (e.g., drinks/drinking day, largest drinks/drinking day, number of drinking days, and heavy drinking frequency). The reference standard was AUD status as determined via a clinical interview (community sample) or a symptom checklist (clinical sample). Setting and Participants Two samples were examined: A large, nationally representative random sample of U.S. community adults who reported past-year drinking ( N =25,778, AUD=20%) and a consecutive drinking clinical sample from a Canadian mental health and addictions inpatient treatment centre ( N =1,341, AUD=82%). Findings All drinking indicators performed much better than chance at classifying AUD (AUCs=0.60-0.92, p s<.0001). Heavy drinking frequency indicators performed optimally in both the community (AUCs=0.78-0.87; accuracy=0.72-0.80) and clinical (AUC=0.85-0.92; accuracy =0.77-0.89) samples. Collectively, the most discriminating drinking behaviors were heavy drinking episodes and exceeding NIAAA drinking low-risk guidelines. No substantive sex differences in optimal cut-offs or variable performance were observed. Conclusions Drinking patterns performed well at classifying AUD in both a nationally representative and large inpatient sample, robustly identifying AUD at rates much better than chance and above accepted benchmarks, with limited differences by sex. Findings broadly support the potential utility of quantitative drinking indicators as being diagnostically informative in clinical settings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.198
GPT teacher head0.430
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes2
Has abstractyes

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