Cannabis use and mood disorders: a systematic review
Bibliographic record
Abstract
Background: Problematic cannabis use is highly prevalent among people with mood disorders. This underscores the need to understand the effects of cannabis and cannabinoids in this population, especially considering legalization of recreational cannabis use. Objectives: We aimed to (1) systematically evaluate cross-sectional and longitudinal studies investigating the interplay between cannabis use, cannabis use disorder (CUD), and the occurrence of mood disorders and symptoms, with a focus on major depressive disorder (MDD) and bipolar disorder (BD) and; (2) examine the effects of cannabis on the prognosis and treatment outcomes of MDD and BD. Methods: Following PRISMA guidelines, we conducted an extensive search for English-language studies investigating the potential impact of cannabis on the development and prognosis of mood disorders published from inception through November 2023, using EMBASE, PsycINFO, PubMed, and MEDLINE databases. Results: Our literature search identified 3,262 studies, with 78 meeting inclusion criteria. We found that cannabis use is associated with increased depressive and manic symptoms in the general population in addition to an elevated likelihood of developing MDD and BD. Furthermore, we observed that cannabis use is linked to an unfavorable prognosis in both MDD or BD. Discussion: Our findings suggest that cannabis use may negatively influence the development, course, and prognosis of MDD and BD. Future well-designed studies, considering type, amount, and frequency of cannabis use while addressing confounding factors, are imperative for a comprehensive understanding of this relationship. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023481634.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".