General risks of harm with cannabinoids, cannabis, and cannabis-based medicine possibly relevant to patients receiving these for pain management: an overview of systematic reviews
Bibliographic record
Abstract
ABSTRACT: The growing demand for improved pain treatments together with expanding legalization of, and access to, cannabinoids, cannabis, and cannabis-based medicines has intensified the focus on risk-benefit considerations in pain management. Given limited harms data from analgesic clinical trials, we conducted an overview of systematic reviews focused on all harms possibly relevant to patients receiving cannabinoids for pain management. This PROSPERO-registered, PRISMA-compliant systematic overview identified 79 reviews, encompassing over 2200 individual reports about psychiatric and psychosocial harms, cognitive/behavioral effects, motor vehicle accidents, cardiovascular, respiratory, cancer-related, maternal/fetal, and general harms. Reviews, and their included studies, were of variable quality. Available evidence suggests variable associations between cannabis exposure (ranging from monthly to daily use based largely on self-report) and psychosis, motor vehicle accidents, respiratory problems, and other harms. Most evidence comes from settings other than that of pain management (eg, nonmedicinal and experimental) but does signal a need for caution and more robust harms evaluation in future studies. Given partial overlap between patients receiving cannabinoids for pain management and individuals using cannabinoids for other reasons, lessons from the crisis of oversupply and overuse of opioids in some parts of the world emphasize the need to broadly consider harms evidence from real-world settings. The advancement of research on cannabinoid harms will serve to guide optimal approaches to the use of cannabinoids for pain management. In the meantime, this evidence should be carefully examined when making risk-benefit considerations about the use of cannabinoids, cannabis, and cannabis-based medicine for chronic pain.
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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".