Cannabis for pain in orthopedics: a systematic review focusing on study methodology
Bibliographic record
Abstract
Background: Medical cannabis use is an emerging topic of interest in orthopedics. Although there is a large amount of literature on medical cannabis use for managing various types of pain, few studies have focused on orthopedic conditions. There is little high-quality evidence in core orthopedic areas. The objective of this study was to summarize the literature on the efficacy of cannabis use for pain related to orthopedic conditions. Methods: We conducted a systematic review of the literature on the use of cannabinoids for pain management in core orthopedic conditions. Two independent reviewers extracted information on reporting quality, risk of bias, drugs, population, control, duration of study, pain outcomes and the authors’ conclusions regarding efficacy for pain outcomes. Results: We identified 33 orthopedic studies, including 21 primary studies and 12 reviews. Study quality was generally low to moderate. Six of the included studies had a control group and 15 were noncontrolled studies. Methodologies, drugs and protocols of administration varied greatly across studies. Study conclusions were generally positive in noncontrolled studies and mixed in controlled studies. Studies using higher doses tended to conclude that cannabis use was effective, but the potential for harmful effects may also be increased with higher doses. Conclusion: Variability in the methodologies used in cannabis research makes it challenging to draw conclusions about dosing, routes and frequency of administration. Most of the existing evidence suggests that medical cannabis use is effective, but this efficacy has been demonstrated only when either there is no comparator or cannabis is compared with placebo. Studies using an active comparator have not demonstrated efficacy. Future research should focus on improving study reporting and methodologic quality so that protocols that optimize pain control while minimizing harmful effects can be determined.
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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 | 0.051 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".