Use of Cannabidiol for the Treatment of Anxiety: A Short Synthesis of Pre-Clinical and Clinical Evidence
Bibliographic record
Abstract
Anxiety disorders have the highest lifetime prevalence of any mental illness worldwide, leading to high societal costs and economic burden. Current pharmacotherapies for anxiety disorders are associated with adverse effects and low efficacy. Cannabidiol (CBD) is a constituent of the Cannabis plant, which has potential therapeutic properties for various indications. After the recent legalization of cannabis, CBD has drawn increased attention as a potential treatment, as the majority of existing data suggest it is safe, well tolerated, has few adverse effects, and demonstrates no potential for abuse or dependence in humans. Pre-clinical research using animal models of innate fear and anxiety-like behaviors have found anxiolytic, antistress, anticompulsive, and panicolytic-like effects of CBD. Preliminary evidence from human trials using both healthy volunteers and individuals with social anxiety disorder, suggests that CBD may have anxiolytic effects. Although these findings are promising, future research is warranted to determine the efficacy of CBD in other anxiety disorders, establish appropriate doses, and determine its long-term efficacy. The majority of pre-clinical and clinical research has been conducted using males only. Among individuals with anxiety disorders, the prevalence rates, symptomology, and treatment response differ between males and females. Thus, future research should focus on this area due to the lack of research in females and the knowledge gap on sex and gender differences in the effectiveness of CBD as a potential treatment for anxiety.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".