Cannabis against chronic muskuloskeletal pain: A scoping review on users and their perceptions
Bibliographic record
Abstract
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 mainly consist of pain management with 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.Objectives Little is known about MC users suffering from CMP. We aimed to increase this knowledge, useful for health care professionals and policy makers considering this treatment, as well as for researchers planning rigorous randomized clinical trials on the effectiveness of MC.Methods We conducted a scoping literature review, according to the methods developed by Arksey and O’Malley, to describe the views and perceptions of patients who had consumed MC to relieve chronic CMP and other non-cancer pain, as well as their demographic characteristics, patterns of MC use, and perceived positive and negative effects.Conclusion Our review shows that MC users are frequently young or middle-aged men, and that the preferred form of use was smoking. Participants of the included studies reported that MC use was helpful in reducing CMP and other chronic non-cancer pain with only minor adverse effects; in addition, they reported improved psychological well-being. Discussion The information from the included studies has several methodological limitations and is exploratory. MC use might, from the perspective of persistent users suffering from CMP and other chronic non-cancer pain, produce more benefits than harms. However, specific results for CMP 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 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| 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".