The role of cannabis in treating anxiety
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cannabis use for medical purposes has become increasingly common, including as treatment for mental health disorders such as anxiety. Unfortunately, the evidence examining its use in mental health has been slow to evolve, but is emerging. Given the widespread use of cannabis, it is important for both clinicians and those who suffer with anxiety to understand the effects of cannabis on symptoms of anxiety. In this review, we present recent, available evidence from animal models, clinical trials, and survey studies and evaluate the contribution of these studies to the current understanding of the role of cannabis in treating anxiety. RECENT FINDINGS: In reviewing recent evidence, we observed significant inconsistencies across findings from preclinical studies. Large-scale surveys suggest that cannabis may be effective in reducing anxiety, however, these results stand in contrast to equivocal findings from clinical trials. SUMMARY: The literature evaluating the efficacy of cannabis in anxiety disorders is in its infancy. The survey data is generally positive. Although, while some animal studies posit cannabis constituents to have anxiolytic effects, others suggest the opposite or null results. Few new clinical trials have been conducted recently, and the extant trials have significant flaws in methodology. Although anecdotal evidence from survey studies, and a small signal found in animal studies and single-dose clinical trials provide early support that cannabis may be effective for alleviating anxiety, ultimately, the current evidence is equivocal. More high-quality clinical trials must be published before sound conclusions regarding the efficacy of cannabis for treating anxiety can be drawn.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".