Medical Cannabis for Chronic Noncancer Pain: A Systematic Review of Health Care Recommendations
Bibliographic record
Abstract
Purpose: Medical cannabis for patients with chronic noncancer pain (CNCP) has been the focus of numerous health care recommendations. We conducted a systematic review to identify and summarize the currently available evidence-based recommendations. Methods: We searched MEDLINE, EMBASE, PsycINFO, the Cochrane database of systematic reviews, and websites for clinical guidelines and recommendations. We summarized the type of the publications, developers, approach of health care recommendation development, year and country of publication, and conditions that were addressed. We categorized the direction and strength of each recommendation. Results: = 11, 92%) of the included recommendations were based on both a systematic review of the best evidence and expert consensus. All the included publications provided a recommendation supporting medical cannabis for CNCP in general and for the specific conditions of neuropathic pain, chronic pain in people living with Human Immunodeficiency Virus (HIV), and chronic abdominal pain, with detailed information sharing and comprehensive consideration of a patient's own values and preferences. Conclusion: Clinicians can attend to the guidance currently offered, being aware that only weak recommendations are available for medical cannabis in patients with CNCP, as a third- or fourth-line therapy. Detailed discussions with patients regarding the benefits in reducing pain and potential adverse effects are required before its prescription.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".