Probability and correlates of transition from cannabis use to DSM‐5 cannabis use disorder: Results from a large‐scale nationally representative study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: It has been previously reported that more than 34% of individuals who use cannabis may qualify for a diagnosis of DSM-IV cannabis abuse or dependence throughout their lifetime. The introduction of the DSM-5 cannabis use disorder (CUD) diagnostic criteria reflects several intrinsic changes in the perception of substance use disorders. However, little is known about the probability of transition from cannabis use to CUD over time nor about the sociodemographic and clinical correlates associated with this transition. DESIGN AND METHODS: Participants were individuals ≥18 years interviewed in the National Epidemiologic Survey on Alcohol and Related Conditions-III in 2012-2013. Measurements included univariable and multivariable discrete-time survival analyses performed to examine the association between previously reported cannabis dependence predictors and the hazards of transitioning from cannabis use to CUD. Survival plots assessed the probability of transition from cannabis use to CUD over time since age of first use and differences in probability between predictor levels. RESULTS: Among lifetime cannabis users (N = 11 272), lifetime probability of transition to CUD was approximately 27%. A higher probability of transition from cannabis use to CUD was observed in the following: men, participants belonging to an ethnic minority group, early-onset cannabis users and individuals who reported experiencing three or more childhood adverse events. DISCUSSION AND CONCLUSIONS: This is the first study to explore transition from cannabis use to the DSM-5 CUD diagnosis. The current study identified specific predictors of this transition, which may assist in targeting at-risk populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".