The Impact of Cannabidiol on Psychiatric and Medical Conditions
Bibliographic record
Abstract
Cannabidiol (CBD) is a substance chemically derived from Cannabis sativa and discussed to be non-psychoactive. According to the FDA, marijuana is classified as a schedule I substance; however, hemp which is defined as extracts from marijuana including cannabinoids containing less than 0.3% tetrahydrocannabinol (THC), is excluded from that controlled substance act and available at local convenience stores in the US as it is seen as an herbal supplement. CBD is purported to be used for various medical and psychiatric conditions: depression, anxiety, post-traumatic stress disorder, Alzheimer's or other cognitive illnesses as well as pain. There is also a new trend to use CBD for the treatment of opioid use disorder. The one CBD product on the market that is FDA approved for the treatment of childhood epilepsy forms Dravet and Lennox-Gastaut syndromes is available under the name Epidiolex. There is a significant difference between this medication and the over-the-counter CBD products that contain very inconsistent strengths of CBD, if they contain it at all, and vary in percentage even from sample to sample. Frequently the so-called CBD products are not containing any CBD at all, but mostly containing THC. This article is a systematic review of literature reviewing the available clinical data on CBD, for use in various medical and psychiatric conditions with focus on a review of the pharmacology and toxicity. Resources used were ORVID, PubMed, MEDLINE, PsychINFO, EMBASE with keywords CBD, cannabidiol, hemp and cannabinoids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| 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) | 0.009 | 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".