Rasch analysis of <scp>DSM</scp> ‐5 alcohol use disorder in a large inpatient sample
Bibliographic record
Abstract
BACKGROUND: In the present study, we extend previous psychometric evaluations of the DSM-5 alcohol use disorder (AUD) criteria using Rasch analysis in a large clinical sample. METHODS: Adult patients with AUD (N = 1101) completed the DSM-5 AUD symptom checklist as part of routine clinical assessment upon admission to an inpatient substance use disorder treatment program. We conducted Rasch analysis of responses to the 11 criteria, examining model fit and item severities. We also examined whether there was evidence of differential item functioning based on sex and age. RESULTS: Results supported the unidimensionality and local independence of the criteria, although some items were a suboptimal fit to the Rasch model. In particular, across all indicators, hazardous use exhibited misfit with model expectations. Additionally, the range of item severities did not span the full range of problem severity within this clinical sample, with many patients at the high end of the severity continuum and no items to differentiate them. There was evidence of differential item functioning by both sex and age, but effect size indices suggested that differences were unlikely to be clinically meaningful. CONCLUSIONS: The present study supports the unidimensionality of the DSM-5 AUD diagnosis, but the misfit of certain items to the Rasch model and the narrow range of item severities suggest that more granular distinctions in AUD may be limited in high-severity samples. The results also suggest that the assumptions of interval-level measurement may not hold in clinical populations.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".