Cannabinoids in Integumentary Wound Care: A Systematic Review of Emerging Preclinical and Clinical Evidence
Bibliographic record
Abstract
This systematic review critically evaluates preclinical and clinical data on the antibacterial and wound healing properties of cannabinoids in integument wounds. Comprehensive searches were conducted across multiple databases, including CINAHL, Cochrane library, Medline, Embase, PubMed, Web of Science, and LILACS, encompassing records up to May 22, 2024. Eighteen studies met the inclusion criteria. Eleven were animal studies, predominantly utilizing murine models (n = 10) and one equine model, involving 437 animals. The seven human studies ranged from case reports to randomized controlled trials, encompassing 92 participants aged six months to ninety years, with sample sizes varying from 1 to 69 patients. The studies examined the effects of various cannabinoid formulations, including combinations with other plant extracts, crude extracts, and purified and synthetic cannabis-based medications administered topically, intraperitoneally, orally, or sublingually. Four animal and three human studies reported complete wound closure. Hemp fruit oil extract, cannabidiol (CBD), and GP1a resulted in complete wound closure in twenty-three (range: 5–84) days with a healing rate of 66–86% within ten days in animal studies. One human study documented a wound healing rate of 3.3 cm2 over 30 days, while three studies on chronic, non-healing wounds reported an average healing time of 54 (21–150) days for 17 patients by oral oils with tetrahydrocannabinol (THC) and CBD and topical gels with THC, CBD, and terpenes. CBD and tetrahydrocannabidiol demonstrated significant potential in reducing bacterial loads in murine models. However, further high-quality research is imperative to fully elucidate the therapeutic potential of cannabinoids in the treatment of bacterial skin infections and wounds. Additionally, it is crucial to delineate the impact of medicinal cannabis on the various phases of wound healing. This study was registered in PROSPERO (CRD42021255413).
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".