Cannabinoids, cannabis, and cannabis-based medicine for pain management: a protocol for an overview of systematic reviews and a systematic review of randomised controlled trials
Bibliographic record
Abstract
Pain is an experience that affects many people worldwide and is associated with higher mortality and lower quality of life. Cannabinoid, cannabis, and cannabis-based medicines (CBMs) are thought to reduce pain, but a proliferation of different products has led to variability in trials, creating a challenge when determining the assessment of efficacy in systematic reviews. We will conduct 2 systematic reviews commissioned by the International Association for the Study of Pain Task Force on the use of cannabinoids, cannabis, and CBMs for pain management: first, an overview review of systematic reviews to summarise the evidence base and second, a systematic review of randomised controlled trials of cannabinoids, cannabis, and CBMs. In these reviews we will determine the harm and benefit of CBM from the current literature and will interpret the findings in light of the quality of evidence and reviews included. We will search online databases and registries in any language for systematic reviews and randomised controlled trials. We will include studies that evaluate any cannabinoid or CBM vs any control for people with acute and chronic pain. Our primary outcomes for both reviews are the number of participants achieving (1) a 30% and (2) 50% reduction in pain intensity, (3) moderate improvement, and (4) substantial improvement. A number of secondary outcome measures will also be included. We will assess risk of bias and quality of evidence. We will analyse data using fixed and random effect models, with separate comparators for cannabis and CBMs. Prospero ID (CRD42019124710; CRD42019124714).
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 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 | 0.118 | 0.169 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.073 | 0.017 |
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".