Pharmacodynamic effects following co-administration of cannabinoids and opioids: a scoping review of human experimental studies
Bibliographic record
Abstract
BACKGROUND: Cannabinoids are increasingly used in the management of chronic pain. Although analgesic potential has been demonstrated, cannabinoids interact with a range of bodily functions that are also influenced by chronic pain medications, including opioids. OBJECTIVE: We performed a scoping review of literature on the pharmacodynamic effects following the co-administration of cannabinoids and opioids. METHODS: We systematically searched EMBASE, PubMed, and PsycINFO for studies that experimentally investigated the co-effects of cannabinoids and opioids in human subjects. Available evidence was summarized by clinical population and organ system. A risk of bias assessment was performed. RESULTS: A total of 16 studies met the inclusion criteria. Study populations included patients with chronic non-cancer and cancer pain on long-term opioid regimens and healthy young adults without prior exposure to opioids who were subject to experimental nociceptive stimuli. Commonly administered cannabinoid agents included Δ9-tetrahydrocannabinol and/or cannabidiol. Co-administration of cannabinoids and opioids did not consistently improve pain outcomes; however, sleep and mood benefits were observed in chronic pain patients. Increased somnolence, memory and attention impairment, dizziness, gait disturbance, and nauseousness and vomiting were noted with co-administration of cannabinoids and opioids. Cardiorespiratory effects following co-administration appeared to vary according to duration of exposure, population type, and prior exposure to cannabinoids and opioids. CONCLUSIONS: The available evidence directly investigating the pharmacodynamic effects following co-administration of cannabinoids and opioids for non-analgesic outcomes is scarce and suffers from a lack of methodological reporting. As such, further research in this area with comprehensive methodologic reporting is warranted.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".