International Association for the Study of Pain Presidential Task Force on Cannabis and Cannabinoid Analgesia: research agenda on the use of cannabinoids, cannabis, and cannabis-based medicines for pain management
Bibliographic record
Abstract
ABSTRACT: The President of the International Association for the Study of Pain established a task force on cannabis and cannabinoid analgesia to systematically examine the evidence on (1) analgesic pharmacology of cannabinoids and preclinical evidence on their efficacy in animal models of injury-related or pathological persistent pain; (2) the clinical efficacy of cannabis, cannabinoids, and cannabis-based medicines for pain; (3) harms related to long-term use of cannabinoids; as well as (4) societal issues and policy implications related to the use of these compounds for pain management. Here, we summarize key knowledge gaps identified in the task force outputs and propose a research agenda for generating high-quality evidence on the topic. The systematic assessment of preclinical and clinical literature identified gaps in rigor of study design and reporting across the translational spectrum. We provide recommendations to improve the quality, rigor, transparency, and reproducibility of preclinical and clinical research on cannabis and cannabinoids for pain, as well as for the conduct of systematic reviews on the topic. Gaps related to comprehensive understanding of the endocannabinoid system and cannabinoid pharmacology, including pharmacokinetics and drug formulation aspects, are discussed. We outline key areas where high-quality clinical trials with cannabinoids are needed. Remaining important questions about long-term and short-term safety of cannabis and cannabinoids are emphasized. Finally, regulatory, societal, and policy challenges associated with medicinal and nonmedicinal use of cannabis are highlighted, with recommendations for improving patient safety and reducing societal harms in the context of pain management.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".