Risks of harm with cannabinoids, cannabis, and cannabis-based medicine for pain management relevant to patients receiving pain treatment: protocol for an overview of systematic reviews
Bibliographic record
Abstract
INTRODUCTION: With the increasing availability of cannabis and cannabinoids and their potential utility for pain treatment, there is a growing need to evaluate the risk-benefit considerations of cannabinoids for the management of pain. As part of the IASP Cannabis and Cannabinoids Task Force, this protocol describes a planned overview of systematic reviews summarizing the risks of harm with cannabinoids that are relevant to patients receiving pain treatment. METHODS: This overview will involve literature searches of several databases and a defined search strategy that will target systematic reviews or meta-analyses of cannabinoids where harms are the primary focus. Data extraction will include various features of the cannabinoid(s) and the harm(s) being studied as well as other methodological features of each included systematic review. Methodological quality of each included review will be assessed using AMSTAR-2 as well as compliance with the PRISMA harms checklist. Prospero registration pending. DISCUSSION: The broad overview of reviews defined by this protocol is expected to synthesize available good quality evidence of harms that will help inform risk-benefit considerations about the use of cannabinoids for pain management.
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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 | 0.080 | 0.107 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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".