A systematic review and meta-analysis of sex differences in cannabis use disorder amongst people with comorbid mental illness
Bibliographic record
Abstract
BACKGROUND: While males are more likely diagnosed with cannabis use disorder (CUD), females are more susceptible to developing and maintaining CUD. Yet, for both sexes, CUD is associated with high rates of comorbid mental illness (MI). OBJECTIVES: To identify and compare sex differences in the prevalence of comorbid CUD amongst individuals with/without MIs. METHODS: This systematic review generated pooled odds ratios (OR) and 95% confidence intervals (CI) from 37 studies (including clinical trials, cohort, and case-control studies) among individuals with and without MIs, quantifying sex differences in rates of comorbid CUD. A meta-analysis was also completed. RESULTS: In the CUD-only group, males were twice as likely to have CUD than females (OR = 2.0, CI = 1.9-2.1). Among MIs, males were more likely than females to have CUD comorbid with schizophrenia (OR ~2.6, CI = 2.5-2.7) and other psychotic, mood, and substance use disorders (1> OR <2.2, CI = 0.7-2.6). The reverse association (females > males) was observed for anxiety disorders and antisocial personality disorder (OR = 0.8, CI = 0.7-1.0). Among females, MIs increased the likelihood of having CUD, except for psychotic disorders and depression. A meta-analysis was inconclusive due to high heterogeneity across studies. Thus, comparisons across MI groups were not possible. CONCLUSION: While males are more likely to be diagnosed with CUD, there are important sex differences in the prevalence of CUD across MI diagnoses that should be taken into account when approaching CUD prevention and determining treatment efficacy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".