Evidence-based Potential Therapeutic Applications of Cannabinoids in Wound Management
Bibliographic record
Abstract
BACKGROUND: Although wound management is a major component of all domains of healthcare, conventional therapeutics have numerous limitations. The endocannabinoid system of the skin, one of the major endogenous systems, has recently been connected to wound healing. Cannabinoids and their interactions with the endogenous chemical signaling system may be a promising therapeutic option because they address some of the fundamental pathways for physiologic derangement that underpin chronic integumentary wounds. RECENT ADVANCES: The therapeutic applications of cannabinoids are increasing because of their legalization and resulting market expansion. Recently, their immunosuppressive and anti-inflammatory properties have been explored for the treatment of wounds that are not effectively managed by conventional medicines. CRITICAL ISSUES: Failure to manage wounds effectively is associated with reduced quality of life, disability, mortality, and increased healthcare expenditures. Therapeutic options that can manage wounds effectively and efficiently are needed. In this review, the authors summarize recent advances on the use of cannabinoids to treat skin disorders with an emphasis on wound management. FUTURE DIRECTIONS: Effective wound management requires medicines with good therapeutic outcomes and minimal adverse effects. Despite the promising results of cannabinoids in wound management, further controlled clinical studies are required to establish the definitive role of these compounds in the pathophysiology of wounds and their usefulness in the clinical setting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.001 | 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".