What Is Known About Users Of Medical Cannabis Against Chronic Musculoskeletal Pain? A Scoping Review Of The Literature
Bibliographic record
Abstract
Introduction Chronic musculoskeletal pain (CMP) is one of the most common causes of chronic pain that leads to reduced physical function. Currently, the pharmacotherapeutic options against CMP are limited and consist mainly in pain management with gabapentinoids or with opioids, which may have major adverse effects. Although the effectiveness of cannabis for medical purposes (or “medical cannabis”, MC) in the treatment of chronic pain still lacks evidence, a number of patients suffering from CMP are exploring this new therapeutic option. Knowledge regarding MC users suffering from CMP is quite limited but necessary to guide prescribers and policy makers considering this treatment as well as researchers planning rigorous randomized clinical trials on the effectiveness of MC. Methods We conducted a scoping literature review to describe the views and perceptions of patients who had consumed MC to relieve CMP, including demographic characteristics, patterns of MC use and perceived positive, negative or adverse effects. Results Forty‐six studies were included in the present scoping review: 21 were conducted in the US, nine in Canada, ten in Europe, two in Australia, one in Israel, and three included data from several countries (up to 43) in Europe and North America. Our review shows that currently typical MC users are mainly young or middle‐aged men (mean age: 28–61), and the preferred form of use is smoking. A majority of the participants of included studies reported that MC use was helpful in reducing chronic pain with only minor adverse effects; in addition, they reported improved psychological well‐being. Conclusions Although the information described in the included studies is still exploratory, MC use seems, from the perspective of persistent MC users suffering from chronic pain, to produce more benefits than harms. Support or Funding Information This study was supported by the Canadian Institutes of Health Research (CIHR). This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".