Cannabis and pain: a scoping review
Bibliographic record
Abstract
For centuries, cannabis has been used with many different purposes, including medicinal use, usually bypassing any formal approval process. However, during the last decade, interest in cannabis in medicine has been increasing, and several countries, including the United States and Canada, have produced their own legislation about marihuana and cannabis-based medicines. Because of this, interest in research has been increasing and evidence about its medical effects is becoming necessary. We conducted a review examining the evidence of cannabis in pain. Cannabis had been shown to be useful in acute and chronic pain, however recently, these results have been controverted. Within the different types of chronic pain, it has a weak evidence for neuropathic, rheumatic pain, and headache, modest evidence for multiple sclerosis related pain, and as adjuvant therapy in cancer pain. There is no strong evidence to recommend cannabis in order to decrease opioids in patients with chronic use. Even though cannabis-based medications appear to be mostly safe, mild adverse effects are common; somnolence, sedation, amnesia, euphoric mood, hyperhidrosis, paranoia, and confusion may limit the use of cannabis in clinical practice. Risks have not been systematically analyzed. Special concern arises on how adverse effect might affect vulnerable population such as elderly patients. More research is needed in order to evaluate benefits and risks, as well as the ideal administration route and dosages. As cannabis use increases in several countries, answers to these questions might be coming soon.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".