Cannabis against chronic musculoskeletal pain: a scoping review on users and their perceptions
Bibliographic record
Abstract
BACKGROUND: Chronic musculoskeletal pain (CMP) may lead to reduced physical function and is the most common cause of chronic non-cancer pain. Currently, the pharmacotherapeutic options against CMP are limited and frequently consist of pain management with non-steroidal anti-inflammatories, gabapentinoids, or opioids, which carry major adverse effects. Although the effectiveness of medical cannabis (MC) for CMP still lacks solid evidence, several patients suffering from it are exploring this therapeutic option with their physicians. OBJECTIVES: Little is known about patients' perceptions of their MC treatment for CMP. We aimed to increase this knowledge, useful for healthcare professionals and patients considering this treatment, by conducting a scoping literature review, following guidance by Arksey and O'Malley, to describe the views and perceptions of adult patients who had consumed MC to relieve chronic CMP. METHODS: Databases (PUBMED, EMBASE, Web of Science) and websites were searched using combinations of controlled and free vocabulary. All studies and study designs reporting on patients' perceptions regarding MC against CMP were considered. Studies had to include adult patients reporting qualitatively or quantitatively, i.e., through questionnaires, on MC use to treat CMP or other non-cancer pain, since studies reporting exclusively on perceptions regarding CMP were very rare. Study characteristics were extracted and limitations of the study quality were assessed. The review includes patients' demographic characteristics, patterns of MC use, perceived positive and negative effects, use of alcohol or other drugs, reported barriers to CM use, and funding sources of the studies. RESULTS: Participants of the 49 included studies reported that MC use helped them to reduce CMP and other chronic non-cancer pain, with only minor adverse effects, and some reported improved psychological well-being. In the included studies, men represent between 18 and 88% of the subjects. The mean age of participants in these studies (42/49) varied between 28.4 and 62.8 years old. The most common route of administration is inhalation. CONCLUSION: MC users suffering from CMP or other chronic non-cancer pain perceived more benefits than harms. However, the information from these studies has several methodological limitations and results are exploratory. These user-reported experiences must thus be examined by well-designed and methodologically sound clinical or observational studies, particularly regarding CMP, where reports are very scarce.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| 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) | 0.004 | 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".