Exploring the Possible Role of Cannabinoids in Managing Post-Cardiac Surgery Complications: A Narrative Review of Preclinical Evidence and a Call for Future Research Directions
Bibliographic record
Abstract
ABSTRACT: Open-heart surgery with cardiopulmonary bypass often leads to complications including pain, systemic inflammation, and organ damage. Traditionally managed with opioids, these pain relief methods bring potential long-term risks, prompting the exploration of alternative treatments. The legalization of cannabis in various regions has reignited interest in cannabinoids, such as cannabidiol, known for their anti-inflammatory, analgesic, and neuroprotective properties. Historical and ongoing research acknowledges the endocannabinoid system's crucial role in managing physiological processes, suggesting that cannabinoids could offer therapeutic benefits in postsurgical recovery. Specifically, cannabidiol has shown promise in managing pain, moderating immune responses, and mitigating ischemia/reperfusion injury, underscoring its potential in postoperative care. However, the translation of these findings into clinical practice faces challenges, highlighting the need for extensive research to establish effective, safe cannabinoid-based therapies for patients undergoing open-heart surgery. This narrative review advocates for a balanced approach, considering both the therapeutic potential of cannabinoids and the complexities of their integration into clinical settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".