Is cannabis treatment for anxiety, mood, and related disorders ready for prime time?
Bibliographic record
Abstract
Anxiety and related disorders are the most common mental conditions affecting the North American population. Despite their established efficacy, first-line antidepressant treatments are associated with significant side effects, leading many afflicted individuals to seek alternative treatments. Cannabis is commonly viewed as a natural alternative for a variety of medical and mental health conditions. Currently, anxiety ranks among the top five medical symptoms for which North Americans report using medical marijuana. However, upon careful review of the extant treatment literature, the anxiolytic effects of cannabis in clinical populations are surprisingly not well-documented. The effects of cannabis on anxiety and mood symptoms have been examined in healthy populations and in several small studies of synthetic cannabinoid agents but there are currently no studies which have examined the effects of the cannabis plant on anxiety and related disorders. In light of the rapidly shifting landscape regarding the legalization of cannabis for medical and recreational purposes, it is important to highlight the significant disconnect between the scientific literature, public opinion, and related policies. The aim of this article is to provide a comprehensive review of the current cannabis treatment literature, and to identify the potential for cannabis to be used as a therapeutic intervention for anxiety, mood, and related disorders. Searches of five electronic databases were conducted (PubMed, MEDLINE, Web of Science, PsychINFO, and Google Scholar), with the most recent in February 2017. The effects of cannabis on healthy populations and clinical psychiatric samples will be discussed, focusing primarily on anxiety and mood disorders.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".