Cannabinoids, cannabis, and cannabis-based medicine for pain management: a systematic review of randomised controlled trials
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
ABSTRACT: Cannabinoids, cannabis, and cannabis-based medicines (CBMs) are increasingly used to manage pain, with limited understanding of their efficacy and safety. We summarised efficacy and adverse events (AEs) of these types of drugs for treating pain using randomised controlled trials: in people of any age, with any type of pain, and for any treatment duration. Primary outcomes were 30% and 50% reduction in pain intensity, and AEs. We assessed risk of bias of included studies, and the overall quality of evidence using GRADE. Studies of <7 and >7 days treatment duration were analysed separately. We included 36 studies (7217 participants) delivering cannabinoids (8 studies), cannabis (6 studies), and CBM (22 studies); all had high and/or uncertain risk of bias. Evidence of benefit was found for cannabis <7 days (risk difference 0.33, 95% confidence interval 0.20-0.46; 2 trials, 231 patients, very low-quality evidence) and nabiximols >7 days (risk difference 0.06, 95% confidence interval 0.01-0.12; 6 trials, 1484 patients, very low-quality evidence). No other beneficial effects were found for other types of cannabinoids, cannabis, or CBM in our primary analyses; 81% of subgroup analyses were negative. Cannabis, nabiximols, and delta-9-tetrahydrocannabinol had more AEs than control. Studies in this field have unclear or high risk of bias, and outcomes had GRADE rating of low- or very low-quality evidence. We have little confidence in the estimates of effect. The evidence neither supports nor refutes claims of efficacy and safety for cannabinoids, cannabis, or CBM in the management of pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.118 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.032 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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