Cannabidiol and cognition: a literature review of human randomized controlled trials
Bibliographic record
Abstract
Cannabidiol (CBD) is a phytocannabinoid without intoxicating properties. While CBD can improve neurophysiological functions and subjective symptoms, its effect on cognitive function remains unclear. We summarized the available randomized controlled trials investigating CBD administration and cognitive function. A review of the literature was conducted using the following keywords on PubMed/Medline: (cannabis OR cannabidiol OR cannabinoid OR CBD OR Δ 9 -tetrahydrocannabinol OR tetrahydrocannabinol) AND (neurology OR brain OR psychiatric OR neuroscience OR psychology OR cognition) AND (human) AND (randomized controlled trial OR RCT). The search yielded 1038 articles with 36 total included for this literature review. The articles included healthy participants, neurological disease, psychiatric disease, psychosis, paranoia, schizophrenia, and drug-use disorders. Studies with healthy participants included a variety of dosing strategies, suggesting an effect on cognitive function and sleep quality. In Parkinson's disease, 75-300 mg CBD resulted in mild improvements in daily life activities. Decreases in subjective anxiety were found in patients with psychiatric disease using CBD doses ranging from 300 to 400 mg. In patients with psychosis and paranoia, 600 mg CBD showed inconsistent results in cognitive function. In patients with schizophrenia, up to 1000 mg CBD per day had minimal effects on cognition. Finally, up to 800 mg CBD had minimal effects on cognitive function in patients with substance use disorders. The findings are limited by utilization of acute dosing, variations in CBD dose, and different routes of administration. Standardized dosing and CBD formulations are needed to assess its efficacy for improving cognition.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".