Pharmacopoeial Standards for Medical Circulation and Use of Cannabis: From Forensic Pharmaceutical and Forensic Narcological Problems of Cannabinoid Addiction to Ensuring Quality Control During the Treatment of Patients with Cannabis Medicines
Bibliographic record
Abstract
The article analyzes current data on the medical use of hemp (Cannabis sativa L.) and its main bioactive components. A historical overview of the use of hemp in ancient civilizations and pharmacopoeias of the 18th-19th centuries is provided, and the reasons for limiting its use in the 20th century are outlined. The pharmacological properties of Δ9-tetrahydrocannabinol and cannabidiol, mechanisms of action through the endocannabinoid system, therapeutic effects, and potential risks are characterized. Evidence-based medicine data on the use of cannabinoids in the treatment of multiple sclerosis, epilepsy, and cancer are presented. Special attention is paid to the pharmacopoeial standards of Cannabis flos, methods of identification and quality control, requirements for the content of active substances and impurities, as well as the prospects for harmonizing the State Pharmacopoeia of Ukraine with the European Pharmacopoeia. Cannabinoid dependence within the ICD-10 and ICD-11, the algorithm for determining its status, and social risks are analyzed. Separately, the international experience of legalizing medical cannabis (USA, Canada, Israel, EU countries) and the possibilities of its implementation in Ukraine are considered. The conclusion is made about the need for an interdisciplinary approach that combines medical, pharmaceutical and legal aspects, which will allow forming a balanced strategy for the implementation of medical cannabis in clinical practice in Ukraine.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".