Comorbidity of Symptoms of Alcohol and Cannabis Use Disorders among a Population-Based Sample of Simultaneous Users. Insight from a Network Perspective
Bibliographic record
Abstract
Research into comorbidity of alcohol and cannabis use disorders has resulted in inconsistent findings, especially among simultaneous users, who used alcohol and cannabis together on a single occasion. This study investigated the association of alcohol and cannabis use disorders among simultaneous users using a network perspective, which considers direct relationships between symptoms. We used a subset of simultaneous alcohol and cannabis users driven from the representative population-based sample of young Swiss men cohort study on substance use risk factors (C-SURF) (n = 1559 at baseline and n = 991 at follow-up). Self-reported symptoms of alcohol and cannabis use disorders were collected. Network analyses included network estimation, visualization, and community detection tests. Alcohol and cannabis use symptoms were separated in two distinct clusters, with few paths between them (eleven positive edges at baseline, three at follow-up). Withdrawal symptoms were likely to connect the two disorders at baseline, but not at follow-up. Alcohol and cannabis use disorders appeared as separate disorders among simultaneous users. Our findings mitigated previous findings on the detrimental association between alcohol and cannabis use. Future studies should incorporate network analyses as a means to study comorbidity in other community and clinical samples to confirm our preliminary findings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".