Medical cannabis or cannabinoids for chronic pain: a clinical practice guideline
Bibliographic record
Abstract
CLINICAL QUESTION: What is the role of medical cannabis or cannabinoids for people living with chronic pain due to cancer or non-cancer causes? CURRENT PRACTICE: Chronic pain is common and distressing and associated with considerable socioeconomic burden globally. Medical cannabis is increasingly used to manage chronic pain, particularly in jurisdictions that have enacted policies to reduce use of opioids; however, existing guideline recommendations are inconsistent, and cannabis remains illegal for therapeutic use in many countries. RECOMMENDATION: The guideline expert panel issued a weak recommendation to offer a trial of non-inhaled medical cannabis or cannabinoids, in addition to standard care and management (if not sufficient), for people living with chronic cancer or non-cancer pain. HOW THIS GUIDELINE WAS CREATED: An international guideline development panel including patients, clinicians with content expertise, and methodologists produced this recommendation in adherence with standards for trustworthy guidelines using the GRADE approach. The MAGIC Evidence Ecosystem Foundation (MAGIC) provided methodological support. The panel applied an individual patient perspective. THE EVIDENCE: This recommendation is informed by a linked series of four systematic reviews summarising the current body of evidence for benefits and harms, as well as patient values and preferences, regarding medical cannabis or cannabinoids for chronic pain. UNDERSTANDING THE RECOMMENDATION: The recommendation is weak because of the close balance between benefits and harms of medical cannabis for chronic pain. It reflects a high value placed on small to very small improvements in self reported pain intensity, physical functioning, and sleep quality, and willingness to accept a small to modest risk of mostly self limited and transient harms. Shared decision making is required to ensure patients make choices that reflect their values and personal context. Further research is warranted and may alter this recommendation.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".