A Randomized Controlled Trial of Three Advanced Wound Dressings in Split-Thickness Skin Grafting Donor Sites—A Personalized Approach?
Bibliographic record
Abstract
Background: Split-thickness skin grafting (STSG) is a frequently used reconstructive technique, and its donor site represents a standardized clinical model to evaluate wound dressings. We compared hydroactive nanocellulose-based, silver-impregnated and ibuprofen-containing foam wound dressings. Methods: A total of 46 patients scheduled for elective surgery were evaluated on the STSG donor site for wound healing (time-to-healing, Hollander Wound Evaluation Scale), pain level (Visual Analogue Scale), and handling (ease of use), as well as scar quality (Patient Scar Assessment Scale, Vancouver Scar Scale) after 3, 6 and 12 months. Results: Almost all dressings compared equally well. We observed statistically relevant differences for pain level favoring the ibuprofen-containing dressing (p = 0.002, ΔAIC = 8.1), and user friendliness in favor of nanocellulose (dressing removal: p = 0.037, ΔAIC = 2.59; application on patient: p = 0.042, ΔAIC = 2.33; wound adhesion: p = 0.017, ΔAIC = 4.16; sensation on skin: p = 0.027, ΔAIC = 3.21). We did not observe any differences for wound healing across all groups. Treatment with hydroactive nanocellulose and the ibuprofen-containing foam revealed statistically relevant better scar appearances as compared to the silver wound dressing (p < 0.001, ΔAIC = 14.77). Conclusion: All wound dressings performed equally well, with the detected statistical differences hinting future directions of clinical relevance. These include the reserved use of silver containing dressings for contaminated or close to contaminated wounds, and the facilitated clinical application of the nanocellulose dressing, which was the only suitable candidate in this series to be impregnated with a range of additional therapeutic agents (e.g., disinfectants and pain-modulating drugs). Personalized donor site management with the tested dressings can meet individual clinical requirements after STSG and improve management strategies and ultimately patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".