Efficacy of Cannabis and its Constituents in Disease Management:Insights from Clinical Studies
Bibliographic record
Abstract
There is a long history of informal use of Cannabis sativa (commonly called cannabis) for many purposes, including treating various ailments worldwide. However, the legalization of cannabis in multiple countries, specifically for medical purposes, has grabbed the researchers' attention to discover the scientific evidence regarding cannabis's beneficial effects. Among over 500 identified compounds (cannabinoids), Δ9-Tetrahydrocannabinol (THC) and cannabidiol (CBD) are two major active cannabinoids derived from cannabis. Cannabinoids exert their effects through cannabinoid receptors (CB1R and CB2R). In the recent past, clinical trials have shown the efficacy of cannabis and cannabinoids for various human ailments, such as cancer, neurological disorders, inflammatory bowel disease, chronic pain, and metabolic disorders. The commonly used constituents and derivatives of cannabis include CBD, THC, THCV, dronabinol, nabilone, and nabiximol. The cannabis constituents have also been used in combination with other agents, such as megestrol acetate, in some clinical trials. The common routes for the administration of cannabis are oral, sublingual, or topical. Cannabis has also been consumed through smoking, inhalation, or with food and tea. A maximum of 572 patients and a minimum of nine patients have participated in a single clinical trial. Cannabis is legalized in some countries with restrictions, such as Belize, Canada, Colombia, Costa Rica, The Czech Republic, Jamaica, Netherlands, South Africa, Spain, and Uruguay. This article provides a compilation of published studies focusing on clinal trials on the therapeutic effects of cannabis. The adverse effects of cannabis and its constituents are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".