Polysubstance Use Disorders in Individuals with Cannabis Use Disorder: Results from a Nationally Representative Sample (National Epidemiologic Survey on Alcohol and Related Conditions)
Bibliographic record
Abstract
Objective: Cannabis use disorder (CUD) is one of the most common substance use disorders (SUDs) worldwide and is frequently associated with high rates of polysubstance use; however, despite rising rates of polysubstance use disorders (PUD), the characteristics of individuals with both CUD and PUD remain unclear. This study, therefore, aims to examine social and clinical characteristics of adults diagnosed with CUD and comorbid PUD. It also aims to assess whether psychiatric disorders are linked to higher odds of PUD among individuals with CUD. Methods: Using a nationally representative U.S. dataset, we assessed 972 individuals with past-year DSM-5 CUD, grouped as CUD only, CUD individuals with one additional SUD (CUD + 1), and CUD individuals with two or more SUDs (CUD + 2). Descriptive statistics summarized social and clinical presentations; multivariate logistic regression examined factors contributing to PUD, controlling for clinical diagnoses and childhood maltreatment. Results: Among CUD individuals, 89.3% ( n = 868) used at least one other substance in the past year, with 34.2% ( n = 332) using two or more. Both the CUD + 1 and CUD + 2 groups experienced significantly more severe childhood maltreatment than CUD only. After adjusting for controls, personality disorders were associated with membership in the CUD + 1 group (odds ratio [OR]: 1.88, p = 0.01); mood disorders were associated with a higher likelihood of being in the CUD + 1 group (OR: 1.50, p = 0.049) and CUD + 2 group (OR: 2.58, p = 0.005). Conclusion: Mood and personality disorders were highly prevalent and linked with PUD in CUD cases. We recommend screening for these disorders in complex CUD cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".