Global experience with medical cannabis use: indications and safety profile (literature review)
Bibliographic record
Abstract
Over the past decades, medical cannabis has received official recognition in more than 40 countries around the world, including Germany, Canada, Italy, and Israel. Medical cannabis is considered a potentially effective remedy for incurable conditions accompanied by pain, spasms, or nausea. Despite its relative safety compared to opioids, the issue of side effects remains relevant. The aim of the study was to systematize information on the main pharmacological properties of medical cannabis and its bioactive components, available dosage forms and medically approved cannabis-based drugs, as well as analyze the evidence base for assessing the effectiveness of medical cannabis in various pathological conditions, taking into account potential risks and safety of use in clinical practice. To achieve the aim of the study, a search for scientific publications was carried out using the queries: medical marijuana, cannabis, cannabinoids, names of medical cannabis drugs, names of cannabinoids in the PubMed, Scopus and Google Scholar databases (2005-2025). 8254 results were obtained for the query. The exclusion criteria were: publications that did not meet the purpose of the review; language of publications (except Ukrainian, English); annotations that did not contain complete information about the results of the studies; publications with closed access. 44 publications were selected for analysis. The article analyzed clinical conditions in which the use of cannabis may be potentially beneficial (mental disorders (post-traumatic stress disorder, anxiety, depression), pain, drug addiction, diseases of the central nervous system and gastrointestinal tract). The safety of medical cannabis is highlighted: risks of addiction, neuro- and cardiotoxicity, psychotic reactions, side effects and features of using by vulnerable groups. Despite the growth of therapeutic use, the evidence base for clinical benefit remains incomplete, and undesirable effects limit its use. The choice of form and route of administration should be adapted to the individual needs of the patient. Thus, the analysis of the literature confirms the significant potential of medical cannabis in the treatment of various diseases, but the safety of its use requires further research.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".