State of the science: cannabis and cannabinoids in palliative medicine—the potential
Bibliographic record
Abstract
Cannabinoids are chemicals derived naturally from the cannabis plant or are synthetically manufactured. They interact directly with cannabinoid receptors or share chemical similarity with endocannabinoids (or both). Within palliative medicine, cannabinoid receptors (CB1 and CB2) may modulate some cancer symptoms: appetite, chemotherapy-induced nausea and vomiting, and mood, pain and sleep disorders. Opioid and cannabinoid receptors have overlapping neuroanatomical receptor distribution, particularly at the dorsal horn, dorsal striatum and locus coeruleus. They have a favourable safety profile compared with opioids, and cannabis-based medicines help chronic pain. While cannabidiol (CBD) has anti-inflammatory properties, tetrahydrocannabinol (THC) is the psychoactive substance for issues such as mood and sleep. Nabiximols (Sativex), a CBD:THC combination, is Food and Drug Administration approved for some multiple sclerosis symptoms and epilepsy. There has been a swift societal evolution in attitudes about use of cannabis and cannabinoid medicines for chronic pain. In the USA, 33 states have now legalised prescription-based medical cannabis for several medical conditions; Canada has had legislation since 2001 authorising medical use. The European Union (EU) recently declared all EU citizens must have access to medical cannabis over the next 4 years. The integration into medicine and routine clinical use of cannabis is fraught with information gaps, regulatory issues and scarcity of research. Each patient should have a comprehensive assessment and risk-benefit discussion before any cannabis-based intervention to avoid possible complications such as hallucinations, psychosis and potential cardiac harm.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".