Topical Glycyrrhiza Glabra gel/guar gum mixture accelerates healing and scar reduction in second-degree burns: A randomized triple-blind controlled trial
Bibliographic record
Abstract
Burn injuries remain a major clinical challenge, particularly in resource-limited settings where safe, effective, and affordable topical therapies are essential. Glycyrrhiza glabra (licorice root) has long been recognized for its anti-inflammatory, antioxidant, and antimicrobial properties, yet its efficacy in human burn wound management remains underexplored. The current study aims to evaluate the therapeutic effects of a 5% G. glabra gel formulated in a guar gum base on wound healing and scar outcomes in patients with second-degree burns. 48 adult patients with superficial or deep second-degree burns (<10% TBSA) were randomly assigned to receive either a topical G.glabra gel (n=24) or standard silver sulfadiazine (SSD) cream (n=24). The primary outcome was time to complete wound healing. Secondary outcomes included changes in Vancouver Scar Scale (VSS) scores and subcomponents (vascularity, height, pliability, pigmentation, pain, and pruritus) assessed on Days 1, 7, 14, and 21. The total phenolic content of the extract was measured spectrophotometrically. The G.glabra group showed significantly faster wound closure (mean 11.75 ± 3.80 vs. 16.33 ± 3.76 days, p<0.001) and greater reductions in total VSS score by Day 21 (p<0.001). All VSS components showed full resolution in the treatment group, compared to partial recovery in controls. Total phenolic content of the extract was 55.96 mg GAE/g, supporting its bioactive potential. Topical application of G.glabra gel significantly accelerated burn wound healing and scar resolution compared to standard treatment. These findings support the integration of phytotherapeutic agents in modern burn care and warrant further large-scale trials.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".