Taxonicity of Cannabis Use Disorder: Findings from Nationally Representative Community Sample and an Inpatient Clinical Sample
Bibliographic record
Abstract
Operational diagnostic definitions of drug addiction have evolved considerably over the last twenty years, including both a categorical one (substance dependence; ICD-11) and predominantly dimensional one (substance use disorder [SUD]; DSM-5). Although cannabis use disorder (CUD) is among the most prevalent SUDs, few studies have directly evaluated whether its underlying structure is categorical or dimensional or investigated this question with a clinical sample. Three taxometric procedures, Mean Above-Minus Below a Cut [MAMBAC], Maximum Eigenvalue [MAXEIG)], Latent Mode [L-Mode], were conducted in two datasets: (1) participants who reported cannabis use in the National Epidemiological Study and Alcohol and Related Conditions III (NESARC-III), a large, nationally representative sample of U.S. community adults (N=3623) and (2) patients reporting pre-admission cannabis use in an inpatient SUD treatment program in Ontario, Canada (N=621). Comparison curve fit indices (CCFI) for the NESARC-III analyses supported dimensional structure: MAMBAC=0.48; MAXEIG=0.30; L-Mode=0.43; Mean CCFI=0.40. CCFI coefficients for the clinical dataset also supported dimensional structure: MMAMBAC=0.09, MAXEIG=0.16, L-Mode=0.29, Mean CCFI=0.18. These results suggest that the latent structure of cannabis addiction is a dimensional construct along a spectrum of severity rather than a dichotomous categorical construct. These findings are more consistent with the DSM-5 conceptualization compared to ICD-11.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".