Use of cannabidiol (CBD) for the treatment of cognitive impairment in psychiatric and neurological illness: A narrative review.
Bibliographic record
Abstract
Cannabidiol (CBD) is one of the major phytocannabinoids present in the cannabis plant, with no acute psychotropic effects and a favorable safety and abuse liability profile. Animal and limited controlled human studies have demonstrated CBD to have analgesic, anxiolytic, anti-inflammatory, antipsychotic, and anticonvulsant effects, to name a few possible indications. There is growing evidence for the use of CBD to treat neurological disorders such as epilepsy, multiple sclerosis, Parkinson's disease, and Alzheimer's disease. It has been suggested that CBD improves cognition and neurogenesis. Cognitive impairment is associated with numerous disorders and can involve deficits in learning, memory, executive functioning, and attention. The purpose of this review will be to evaluate the available preclinical and clinical data on CBD for the treatment of the cognitive impairment associated with several disorders including schizophrenia, epilepsy, Alzheimer's disease, and others. Preclinical, but not clinical, studies found evidence for an improvement in cognitive performance after treatment with CBD. More research is needed to determine whether CBD can be effectively used as a monotherapy to treat cognitive dysfunction. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".