Clinical and Research Perspectives of the Use of Cannabinoids In the Treatment of Mental Disorders: Systematic Review
Bibliographic record
Abstract
Mental disorders such as posttraumatic stress disorder (PTSD), psychosis, anxiety, and attention-deficit/hyperactivity disorder (ADHD) are significant global health burdens. While conventional pharmacotherapies and psychotherapies offer symptom relief, up to one-third of patients exhibit inadequate response or intolerable side effects, prompting the exploration of alternative or adjunctive treatments.The researchers conducted a comprehensive literature search in January 2024 across PubMed, Scopus, and EBSCOhost. They screened peer-reviewed studies published in English between 2014 and 2023, using predefined eligibility criteria. The team extracted data using Cochrane-based templates and performed quality assessments with the RoB 2 tool for randomised trials and the Newcastle-Ottawa Scale for observational studies. Finally, they conducted a narrative synthesis based on diagnostic categories.The researchers included ten studies—nine randomised controlled trials and one observational study—that examined cannabinoid interventions in PTSD, psychosis-spectrum disorders, ADHD, and social anxiety. Cannabidiol (CBD) was the most commonly studied compound. Neurobiological improvements were consistently observed in psychosis and PTSD, while clinical symptom reduction was more evident in social anxiety and ADHD. Although findings were heterogeneous, CBD demonstrated favourable safety across all studies, with mild or no adverse effects reported.CBD appears to be safe and shows therapeutic promise in certain psychiatric conditions, particularly for neurobiological modulation in psychosis and PTSD, symptom reduction in social anxiety, and behavioural improvements in adult ADHD. However, evidence remains preliminary. Standardised, large-scale trials are needed to confirm efficacy, refine dosing, and guide clinical use.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".