Dimensionality and stages of severity of DSM-5 criteria in an international sample of alcohol-consuming individuals
Bibliographic record
Abstract
INTRODUCTION: The DSM-5 alcohol use disorder (AUD) criteria proposal contains 11 criteria that include most of the DSM-IV abuse and dependence criteria plus craving. The aims of the current study in a large and international alcohol-consuming sample were to confirm the dimensionality of the DSM-5 AUD criteria and to differentiate grades of severity of DSM-5 AUD in subjects who pass the proposed DSM-5 diagnostic threshold of two criteria. METHOD: We used the World Health Organization (WHO)/International Society on Biomedical Research on Alcoholism (ISBRA) Study on State and Trait Markers of Alcohol Use and Dependence dataset. Subjects included in the analyses were aged ≥ 18 years and were recruited in five countries: Australia, Brazil, Canada, Finland and Japan. Assessment of AUD and additional characteristics was conducted using an adapted version of the Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS). Dimensionality of the DSM-5 criteria was evaluated using factor analysis and item response theory (IRT) models. The IRT results led to the classification of AUD patients into three severity groups. External validators were used to differentiate statistically across subgroups. RESULTS: A total of 1424 currently drinking individuals were included in the analyses. Factor and IRT analyses confirmed the dimensional structure of DSM-5 AUD criteria. More than 99% of the subjects could be allocated to one of the suggested severity subgroups. The magnitude of the external validators differed significantly across the severity groups. CONCLUSIONS: The results confirm the dimensional structure of the proposed DSM-5 AUD criteria. The suggested stages of severity (mild, moderate and severe) may be useful to clinicians by grouping individuals not only in the mild but also in the moderate to severe spectrum of DSM-5 AUD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".