Analgesic efficacy of cannabinoids for acute pain management after surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Evidence regarding the role of cannabinoids in managing acute postoperative pain is conflicting. The purpose of this systematic review and meta-analysis was to determine the analgesic efficacy of perioperative cannabinoid compounds for acute pain management after surgery. Methods Original research articles evaluating the addition of cannabinoids to standard opioid-based systemic analgesia (Control) in the postoperative period were sought. Our primary outcomes were cumulative oral morphine equivalent consumption and rest pain severity at 24 hours postoperatively. We also assessed analgesic consumption in the postanesthesia care unit (PACU), pain scores in PACU, 6 and 12 hours postoperatively, and opioid-related and cannabinoid-related side effects, patient satisfaction, and quality of recovery as secondary outcomes. Results Eight randomized controlled trials (924 patients) and four observational studies (4259 patients) were analyzed and included. There were insufficient data to pool for quantification of differences in cumulative oral morphine equivalent consumption and rest pain severity at 24 hours postoperatively with the addition of cannabinoids in comparison to Control. Qualitative synthesis revealed no differences in cumulative oral opioid consumption or pain at rest 24 hours postoperatively with the addition of cannabinoids in comparison to Control. Patients receiving cannabinoids appeared to have an increased weighted mean difference 95% CI of pain at 12 hours by 0.83 cm (0.04 to 1.63) (p = 0.04). Patients receiving cannabinoids also appeared to have 3.24 times increased odds of developing hypotension postoperatively (95% CI 1.12 to 9.36) (p = 0.03). Qualitative and quantitative synthesis revealed no differences in any other secondary outcomes. Conclusions Our quantitative and qualitative review of the literature suggests that the analgesic role of perioperative cannabinoid compounds is limited, with no clinically important benefits detected when cannabinoids are added to traditional systemic analgesics compared with traditional systemic analgesics alone. Notably, there appears to be a signal towards increased postoperative pain and hypotension associated with the addition of perioperative cannabinoids to traditional systemic analgesics. These results do not support the routine use of cannabinoids to manage acute postoperative pain at the present time.
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.
How this classification was reachedexpand
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.007 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".