Opioid use disorder in two samples of the Lebanese population: scale validation and correlation with sleep and mood disorders
Bibliographic record
Abstract
BACKGROUND: The revised Opioid Risk Tool (ORT-OUD) is a brief, self-report scale designed to provide clinicians with a simple, validated method to screen for the risk of developing an Opioid Use Disorder (OUD) in patients without a prior history of substance abuse. This study aimed to translate and validate the Arabic version of ORT-OUD in the Lebanese population and assess its clinical validity in a sample of patients with OUD. METHODS: This cross-sectional study in the Lebanese population used several validated scales to assess the risk of OUD, including the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). Other tools evaluated chronotype and sleep and mood disturbances. Principal component analysis with Varimax rotation was applied to assess ORT-OUD construct validity. Convergent validity with the Arabic version of ASSIST was evaluated. The ORT-OUD criterion validity was then assessed in a clinical sample of patients with OUD. RESULTS: This study included 581 participants. The prevalence of the OUD risk in the Lebanese population using the ORT-OUD scale and the ASSIST-opioids scale was estimated at 14.5% and 6.54%, respectively. No items of the ORT-OUD were removed; all items converged over a solution of four factors with an eigenvalue > 1, explaining a total of 68.2% of the variance (Cronbach's alpha = 0.648). The correlation coefficients between the ORT-OUD total score and ASSIST subscales were as follows: ASSIST-opioids (r = 0.174; p = < 0.001), ASSIST-sedatives (r = 0.249; p < 0.001), and ASSIST-alcohol (r = 0.161; p = < 0.001). ORT-OUD clinical validation showed a correlation with ASSIST-opioids (r = 0.251; p = 0.093) and ASSIST-sedatives (r = 0.598; p < 0.001). Higher ORT-OUD scores were associated with a family and personal history of alcohol and substance consumption and higher insomnia and anxiety scores. CONCLUSIONS: This study is the first to validate the Arabic version of ORT-OUD in the Lebanese population, an essential step towards improving the detection and management of OUD in this population.
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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".