Cannabis and Cannabinoids in Mood and Anxiety Disorders: Impact on Illness Onset and Course, and Assessment of Therapeutic Potential
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cannabis use is common in people with and mood and anxiety disorders (ADs), and rates of problematic use are higher than in the general population. Given recent policy changes in favor of cannabis legalization, it is important to understand how cannabis and cannabinoids may impact people with these disorders. We aimed to assess the effects of cannabis on the onset and course of depression, bipolar disorder, ADs, and post-traumatic stress disorder (PTSD), and also to explore the therapeutic potential of cannabis and cannabinoids for these disorders. METHODS: A systematic review of the literature was completed. The PubMed® database from January 1990 to May 2018 was searched. We included longitudinal cohort studies, and also all studies using cannabis or a cannabinoid as an active intervention, regardless of the study design. RESULTS: Forty-seven studies were included: 32 reported on illness onset, nine on illness course, and six on cannabinoid therapeutics. Cohort studies varied significantly in design and quality. The literature suggests that cannabis use is linked to the onset and poorer clinical course in bipolar disorder and PTSD, but this finding is not as clear in depression and anxiety disorders (ADs). There have been few high-quality studies of cannabinoid pharmaceuticals in clinical settings. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: These conclusions are limited by a lack of well-controlled longitudinal studies. We suggest that future research be directed toward high-quality, prospective studies of cannabis in clinical populations with mood and ADs, in addition to controlled studies of cannabinoid constituents and pharmaceuticals in these populations. (Am J Addict 2019;00:00-00).
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".