Efficacy, tolerability and safety of cannabis‐based medicines for chronic pain management – An overview of systematic reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medicinal cannabis has already entered mainstream medicine in some countries. This systematic review (SR) aimed at evaluating the efficacy, acceptability and safety of cannabis-based medicines for chronic pain management. Qualitative systematic review of SRs of randomized controlled trials with cannabis-based medicines for chronic pain management. The Cochrane databases of SRs, Database of Abstracts of Reviews of Effects and PubMed were searched for SR published in the period January 2009 to January 2017. Assessment of the methodological quality of SR was performed by the AMSTAR checklist. Out of 748 papers identified, 10 SRs met the inclusion criteria. The methodological quality was high in four and moderate in six SRs. There were inconsistent findings of four SRs on the efficacy of cannabis-based medicines in neuropathic pain and of one SR for painful spasms in multiple sclerosis. There were consistent results that there was insufficient evidence of any cannabis-based medicine for pain management in patients with rheumatic diseases (three SRs) and in cancer pain (two SRs). Cannabis-based medicines undoubtedly enrich the possibilities of drug treatment of chronic pain conditions. It remains the responsibility of the health care community to continue to pursue rigorous study of cannabis-based medicines to provide evidence that meets the standard of 21st century clinical care. SIGNIFICANCE: We provide an overview of systematic reviews on the efficacy, tolerability and safety of cannabis-based medicines for chronic pain management. There are inconsistent findings of the efficacy of cannabinoids in neuropathic pain and painful spasms in multiple sclerosis. There are inconsistent results on tolerability and safety of cannabis-based medicines for any chronic pain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it