Advances in the Use of Electrospun Nanofibrous Polymeric Matrix for Dermal Healing at the Donor Site After the Split-Thickness Skin Graft Excision: A Prospective, Randomized, Controlled, Open-Label, Multicenter Study
Bibliographic record
Abstract
Dressings used to manage donor site wounds (DSWs) have up to 40% of patients experiencing complications that may cause suboptimal scarring. We evaluated the efficacy and safety of a portable electrospun nanofibrous matrix that provides contactless management of DSWs compared with standard dressing techniques. This study included adult patients who underwent an excised split-thickness skin graft (STSG) with a DSW area of 10 to 200 cm2. Patients were allocated into two groups; ie, the nanofiber group managed with a nanofibrous polymer-based matrix, and the control group managed using the standard of care such as Jelonet® or Biatain® Ibu dressing. Primary outcomes were postoperative dermal healing efficacy assessed by Draize scores. The time to complete re-epithelialization was also recorded. Secondary outcomes included postoperative adverse events, pain, and infections during the first 21 days and extended 12-month follow-up. The itching and scarring were recorded during the extended follow-up (months 1, 3, 6, 9, and 12) using Numerical-Analogue-Score and Vancouver scores, respectively. The nanofiber and control groups included 21 and 20 patients, respectively. The Draize dermal irritation scores were significantly lower in the nanofiber vs control group (Z = -2.509; P = .028) on the first postoperative day but became similar afterward (Z ≥ -1.62; P ≥ .198). In addition, the average time to re-epithelialization was similar in the nanofiber (17.9 ± 4.4 days) and control group (18.3 ± 4.5 days; Z = -0.299; P = .764), so were postoperative adverse events, pain, and infection incidence, itching and scarring. The safety and efficacy of electrospun nanofibrous matrix are similar to standard wound care allowing its use as an alternative donor site dressing following the STSG excision.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".