Validation of the Alcohol Use Disorders Identification Test and the Drug Abuse Screening Test in First Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: To determine the validity and reliability of the Alcohol Use Disorders Identification Test (AUDIT) and Drug Abuse Screening Test (DAST) for detecting alcohol and drug use disorders, respectively, in a population with first-episode psychosis (FEP). METHOD: Subjects with FEP completed the AUDIT and DAST and were divided into groups according to the presence or absence of a Structured Clinical Interview for DSM-IV (SCID) diagnosis of either current alcohol or drug misuse. The data were analyzed to see whether AUDIT and DAST scores were predictive of SCID diagnosis. RESULTS: Patients with alcohol-related SCID diagnoses and those with drug-related SCID diagnoses scored significantly higher on the AUDIT and DAST, respectively, than the group without the respective SCID diagnosis (P < 0.001 in both cases). The AUDIT functioned best with a problem drinking cut-off score of 10 (sensitivity, 85%; specificity, 91%). The DAST functioned best with a problem drug use cut-off score of 3 (sensitivity, 85%; specificity, 73%). The area under the receiver operating characteristic curve was 0.86 for the AUDIT and 0.83 for the DAST. CONCLUSION: The DAST and AUDIT may reliably identify FEP patients with substance abuse.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".