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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".