Medical Cannabis Use in Palliative Care: Review of Clinical Effectiveness and Guidelines – An Update
Bibliographic record
Abstract
Palliative care is defined by the World Health Organization as “an approach that improves the quality of life of patients and their families facing the problem associated with life-threatening illness…”. The last days and hours of a person’s life can be associated with immense physical as well as emotional suffering. Relief of pain and other distressing symptoms, and enhancement of quality of life, are among the essential elements of good palliative care.Palliative care could benefit an estimated 69% to 82% of dying individuals in Canada. As Canada’s population ages, with increasing prevalence of chronic conditions and treatments resulting in prolonged life, it is expected that there will be an increased need for palliative care services.Approximately 9% of Canadians (or 2.7 million) reported using cannabis for medical purposes in the first half of 2019. Herbal cannabis (cannabis sativa) contains hundreds of pharmacological components, many of which are not well-characterized. Tetrahydrocannabinol (THC) is the most prevalent pharmacologically active compound and is primarily responsible for the psychoactive and physical effects of cannabis. Cannabidiol (also commonly referred to as CBD) is the second most prevalent. It has very little if any psychotropic effects. Quantity and ratio of these and other components can vary considerably between plants and even within the same plant. Two prescription cannabinoids are currently marketed in Canada: Nabiximols (Sativex) which contains THC and cannabidiol, and Nabilone (Cesamet) which is a synthetic cannabinoid. Dronabinol (Marinol), synthetic THC, was withdrawn from the Canadian market however it is available in other jurisdictions. For the purposes of this report, medical cannabis refers to use of the cannabis plant or its extracts or synthetic cannabinoids for medical purposes.Medical cannabis may be of value for a number of conditions, including but not limited to pain, nausea and vomiting, depression, anxiety and appetite stimulation. Adverse effects of cannabis are very common, developing in 80% to 90% of patients. These include but are not limited to psychiatric disturbances, sedation, speech disorders, impaired memory, dizziness, ataxia, addiction, irritability, and driving impairment. Risk of adverse effects is likely lower with cannabidiol alone as compared to THC., The potential for drug interactions is also an important concern., These risks must be considered along with the an apparent lack of evidence surrounding effectiveness of medical cannabis in many conditions for which its use is promoted.This report updates and expands on a previous summary of abstracts report.The objective of the report is to review evidence and guidelines for use of medical cannabis in the palliative care setting.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".