Organizational and legal measures to prepare the system of palliative and hospice care of Ukraine for the widespread use of medical cannabis
Bibliographic record
Abstract
In Ukraine, the procedure for the legalization of medical cannabis, which is needed for approximately 6 million patients with cancer in the palliative stages, multiple sclerosis, epilepsy, lateral amniotic sclerosis, fibromyalgia, arthritis, HIV/AIDS, glaucoma, post-traumatic stress disorder, Alzheimer's, Parkinson's disease, Tourette, Lennox-Gastaut, Dravet syndromes, irritable bowel, back pain, chronic pain due to spinal cord injuries, diabetic neuropathy, postherpetic neuralgia, is being completed. Cannabis is necessary for such patients to overcome spasticity, chronic pain, nausea, vomiting, anorexia, increased eye pressure. It can be a supplement to treatment with other pharmaceuticals or an alternative to them. On the eve of the entry into force of the relevant law, it is necessary to determine the main directions for the rapid development of the necessary by-laws (clinical protocols, instructions, etc.) for the rapid start of the wide use of cannabis in clinical practice, in particular in palliative medicine. Using the methods of systematic analysis and bibliosemantic, a study of scientific literary sources in Google Scholar and PubMed was conducted to study the main properties of medical cannabis, the medical and social risks of its use, in particular side effects, the increase in illegal recreational use of herbal cannabis. The experience of other countries where medical cannabis has already been legalized (USA, Canada, Australia, Denmark, Germany, Israel, Switzerland) has been studied. The list of normative legal acts of Ukraine that can regulate the use of medical cannabis has been defined: 27 evidence-based clinical guidelines; 27 standards and protocols of medical care. Possible scenarios are identified and the necessary measures are proposed for the adoption of legal acts for the final decriminalization of cannabis, the determination and forecasting of the need for palliative patients, the creation of conditions for the cultivation of Ukrainian herbal cannabis and the manufacture of domestic pharmaceuticals, reimbursement of their cost to patients. Keywords: chronic pain, narcotic painkillers, marijuana, PTSD, anorexia.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".