Evaluation of Dried Amniotic Membrane on Wound Healing at Split-Thickness Skin Graft Donor Sites: A Randomized, Placebo-Controlled, Double-blind Trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of amniotic membrane (AM) at split-thickness skin graft (STSG) donor sites. METHODS: This double-blind randomized controlled trial was conducted on 35 eligible participants referred to the burn unit of Vasei Hospital of Sabzevar, Iran, during 2017 and 2018. Each STSG donor site was divided into two sides, and the respective halves were covered with either a dried AM or petrolatum gauze (control). Outcomes were evaluated on postprocedure days 10, 20, and 30 using the Vancouver Scar Scale. RESULTS: The mean age of the patients was 39.4 ± 13.97 years, and 62.8% (n = 22) were male. There was no statistically significant difference in wound healing rate on day 10 (P = .261), 20 (P = .214), or 30 (P = .187) between groups. The intervention group had significantly better epithelialization than the control group on day 10 (investigator 1, 1.62 ± 0.59 vs 1.40 ± 0.88 [P = .009); investigator 2, 1.22 ± 0.84 vs 0.91 ± 0.85 [P = .003]), as well as pain reduction (P < .001 during the follow-up period). However, there was no statistically significant difference between groups in terms of pigmentation or vascularization (P > .05). CONCLUSIONS: Findings suggest that the use of AM is not superior to petrolatum gauze in terms of STSG healing rate; however, AM achieved better pain reduction and epithelialization on day 10.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".