An Empirical Study of the Cut-Off Point for the Iranian Version of Alcohol Use Disorders Identification Test (AUDIT)
Bibliographic record
Abstract
Background: AUDIT is constructed to be able to identify hazardous drinking and less severe alcohol-related problems. The original AUDIT was shown to have a cut-off score of 8 and above for identifying hazardous or harmful alcohol consumption. The aim of this study was to establish the optimal cut-off point of the Persian version of Alcohol Use Disorders Identification Test (AUDIT) in psychiatric out-patients. Methods: Participants were a sample of consecutive patients at Imam Hossein Hospital (Tehran/Iran). They consisted of 99 patients, 49 of them diagnosed with alcohol dependency and 50 patients randomly selected from a sample of patients using alcohol but with other primary diagnoses. All statistics including means and standard deviations as well as medians and interquartile range were calculated in SPSS 24 software environment. Results: A Receiver Operating Curve analysis showed that by using a 20-point cut-off, the AUDIT had an optimal combination of sensitivity (.92) and specificity (.74). The rate of discrimination was .88. Conclusions: Given the high sensitivity and acceptable specificity of the AUDIT, the test can be used as an effective instrument for identification of alcohol use disorders in the Persian psychiatric out-patient population. Furthermore, the receiver operating curve found in this study resembles the one found in previous studies despite the differences in alcohol cultures between Iran and countries with higher alcohol consumption.
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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 | 0.040 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".