MétaCan
Menu
Back to cohort
Record W2981662240 · doi:10.1111/add.14864

Identifying an accurate self‐reported screening tool for alcohol use disorder: evidence from a Swiss, male population‐based assessment

2019· article· en· W2981662240 on OpenAlexaff
Stéphanie Baggio, Bastien Trächsel, Valentin Rousson, Stéphane Rothen, Joseph Studer, Simon Marmet, Patrick Heller, Frank Sporkert, Jean‐Bernard Daeppen, Gerhard Gmel, Katia Iglesias

Bibliographic record

VenueAddiction · 2019
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Research Foundation
KeywordsAlcohol use disorderMedicineGold standard (test)PhosphatidylethanolEthyl glucuronidePopulationPsychiatryAlcoholClinical psychologyEnvironmental healthInternal medicineAlcohol consumption

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Short screenings for alcohol use disorder (AUD) are crucial for public health purposes, but current self-reported measures have several pitfalls and may be unreliable. The main aim of our study was to provide empirical evidence on the psychometric performance of self-reports currently used. Our research questions were: compared with a gold standard clinical interview, how accurate are (1) self-reported AUD, (2) self-reported alcohol use over time and (3) biomarkers of alcohol use among Swiss men? Finally, we aimed to identify an alternative screening tool. DESIGN: A single-center study with a cross-sectional design and a stratified sample selection. SETTING: Lausanne University Hospital (Switzerland) from October 2017 to June 2018. PARTICIPANTS: We selected participants from the French-speaking participants of the ongoing Cohort Study on Substance Use and Risk Factors (n = 233). The sample included young men aged on average 27.0 years. MEASUREMENTS: We used the Diagnostic Interview for Genetic Studies as the gold standard for DSM-5 AUD. The self-reported measures included 11 criteria for AUD, nine alcohol-related consequences, and previous 12 months' alcohol use. We also assessed biomarkers of chronic excessive drinking (ethyl glucuronide and phosphatidylethanol). FINDINGS: None of the self-reported measures/biomarkers taken alone displayed both sensitivity and specificity close to 100% with respect to the gold standard (e.g. self-reported AUD: sensitivity = 92.3%, specificity = 45.8%). The best model combined eight self-reported criteria of AUD and four alcohol-related consequences. Using a cut-off of three, this screening tool yielded acceptable sensitivity (83.3%) and specificity (78.7%). CONCLUSIONS: Neither self-reported alcohol use disorder nor heavy alcohol use appear to be adequate to screen for alcohol use disorder among young men from the Swiss population. The best screening alternative for alcohol use disorder among young Swiss men appears to be a combination of eight symptoms of alcohol use disorder and four alcohol-related consequences.

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)0.011
metaresearch head score (Gemma)0.023
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 score0.018
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.104
GPT teacher head0.370
Teacher spread0.266 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAddictionSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207