Cannabidiol as a Treatment for Mood Disorders: A Systematic Review: Le cannabidiol comme traitement des troubles de l’humeur: une revue systématique
Bibliographic record
Abstract
Objective: To review the current evidence for efficacy of cannabidiol in the treatment of mood disorders. Methods: We systematically searched PubMed, Embase, Web of Science, PsychInfo, Scielo, ClinicalTrials.gov , and The Cochrane Central Register of Controlled Trials for studies published up to July 31, 2019. The inclusion criteria were clinical trials, observational studies, or case reports evaluating the effect of pure cannabidiol or cannabidiol mixed with other cannabinoids on mood symptoms related to either mood disorders or other health conditions. The review was reported in accordance with guidelines from Preferred Reporting Items for Systematic reviews and Meta-Analyses protocol. Results: Of the 924 records initially yielded by the search, 16 were included in the final sample. Among them, six were clinical studies that used cannabidiol to treat other health conditions but assessed mood symptoms as an additional outcome. Similarly, four tested cannabidiol blended with Δ-9-tetrahydrocannabinol in the treatment of general health conditions and assessed affective symptoms as secondary outcomes. Two were case reports testing cannabidiol. Four studies were observational studies that evaluated the cannabidiol use and its clinical correlates. However, there were no clinical trials investigating the efficacy of cannabidiol, specifically in mood disorders or assessing affective symptoms as the primary outcome. Although some articles point in the direction of benefits of cannabidiol to treat depressive symptoms, the methodology varied in several aspects and the level of evidence is not enough to support its indication as a treatment for mood disorders. Conclusions: There is a lack of evidence to recommend cannabidiol as a treatment for mood disorders. However, considering the preclinical and clinical evidence related to other diseases, cannabidiol might have a role as a treatment for mood disorders. Therefore, there is an urgent need for well-designed clinical trials investigating the efficacy of cannabidiol in 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".