Could Cannabidiol be a treatment for COVID-19-related anxiety disorders?
Bibliographic record
Abstract
COVID-19-related anxiety and post-traumatic stress symptoms (PTSS) or disorder (PTSD) are likely to be a significant long-term issue emerging from the current pandemic. We hypothesise that cannabidiol (CBD), a chemical isolated from Cannabis Sativa with reported anxiolytic properties, could be a therapeutic option for the treatment of COVID-19-related anxiety disorders. In the global over-the-counter CBD market, anxiety, stress, depression and sleep disorders are consistently the top reasons people use CBD. In small randomised, controlled clinical trials, CBD reduces anxiety in healthy volunteers, patients with social anxiety disorder, those at clinical high risk of psychosis, in patients with Parkinson’s disease, and in individuals with heroin use disorder. Case reports and series support these findings, extending to patients with anxiety and sleep disorders, Crohn’s disease, depression and in PTSD. Preclinical studies reveal the molecular targets of CBD in these indications as the cannabinoid receptors type 1 and 2 (CB1 and CB2) receptors (mainly in fear memory processing), serotonin 5HT1a receptors (mainly in anxiolysis) and peroxisome proliferator-activated receptor gamma (PPARγ) (mainly in the underpinning anti-inflammatory/anti-oxidant effects). Observational and preclinical data also support CBD’s therapeutic value in improving sleep (increased sleep duration/quality and reduction in nightmares) and depression, often comorbid with anxiety. Together these features of CBD to reduce anxiety and depression, and improve sleep disturbances, could be an attractive novel therapeutic option in relieving COVID-related post-traumatic stress symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".