Topical Tranexamic Acid, Adrenaline and Bupivacaine Solution for Pain Management and Healing in Split‐Thickness Skin Graft Donor Sites: An Open‐Label Interventional Study With Randomised Side Allocation
Bibliographic record
Abstract
Split-thickness skin graft donor site wounds present significant challenges in pain management and healing optimization. This intra-individual comparative study evaluated the efficacy and safety of a novel topical solution containing tranexamic acid, adrenaline and bupivacaine versus standard paraffin-chlorhexidine dressings, with side allocation determined by computer randomisation after graft harvesting. Twelve patients received standardised solution application on one donor site and standard treatment on the contralateral site, with each side's dressing changes performed according to protocol. The treatment group demonstrated significantly lower mean pain scores across all time intervals (1.1 vs. 5.3 at 24 h, p < 0.001). Mean epithelialization rates at Days 10-14 were higher in the treatment group (97.1% vs. 94.8%, p < 0.05), with faster time to complete healing (median 12 vs. 16 days, p = 0.002). No significant hemodynamic changes occurred following solution application, with only one case of transient tachycardia reported. Vancouver Scar Scale scores at eight weeks showed a trend favouring the treatment (3.8 vs. 4.2, p = 0.15), although this difference was not statistically significant. No infections were observed in either group. These findings suggest that this novel topical solution with transparent film dressing effectively reduces pain and accelerates healing in donor site wounds without compromising safety, providing a promising new option for managing these challenging surgical wounds.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".