Long‐term observational studies with cannabis‐based medicines for chronic non‐cancer pain: A systematic review and meta‐analysis of effectiveness and safety
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This systematic review evaluated the effectiveness, tolerability and safety of cannabis-based medicines (CbMs) for chronic non-cancer pain (CNCP) in long-term observational studies. DATABASES AND DATA TREATMENT: CENTRAL, EMBASE and MEDLINE were searched until December 2021. We included prospective observational studies with a study duration ≥26 weeks. Pooled estimates of event rates of categorical data and standardized mean differences (SMD) of continuous variables were calculated using a random effects model. RESULTS: Six studies were included with 2686 participants, with study duration ranging between 26 and 52 weeks. Pain conditions included nociceptive, nociplastic, neuropathic and mixed pain mechanisms. The certainty of evidence for every outcome was very low. The weighted mean difference of mean pain reduction was 1.75 (95% confidence interval [CI] 0.72 to 2.78) on a 0-10 scale. 20.8% (95% CI 10.2% to 34.0%) of patients reported pain relief of 50% or greater. The effect size for sleep problems was moderate and for depression and anxiety was low. Study completions was reported for 53.3% (95% CI 26.8% to 79.9%) of patients, with dropouts of 6.8% (95% CI 4.3% to 9.7%) due to adverse events. Serious adverse events occurred in 3.0% (95 CI 0.02% to 12.8%) and 0.3% (95% CI 0.1% to 0.6%) of patients died. CONCLUSIONS: Information included in observational studies should be regarded with caution. Within the context of observational studies. CbMs had positive effects on multiple symptoms for some CNCP patients and were generally well tolerated and safe. SIGNIFICANCE: There is very low quality evidence for the long-term effectiveness (pain, sleep, mood, health-related quality of life), tolerability and safety of medical cannabis for chronic non-cancer pain (CNCP) according to reports of prospective observational studies. Predefined criteria of a large magnitude of effect size in these types of studies were not met. Nevertheless, long-term medical cannabis therapy can be considered in some carefully selected and monitored patients with CNCP.
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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 | 0.027 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".