Evaluation of the efficacy of Agicoat in the treatment of partial-thickness skin graft donor sites of burn patients.
Bibliographic record
Abstract
INTRODUCTION: Burns is the most common condition that requires extensive skin grafting. Treatment of burns is associated with long hospital stays, expensive medications, multiple surgeries, and long-term rehabilitation. Rapid healing of skin donor areas in partial-thickness burn wounds is important for the patient. Partial-thickness skin grafting is a technique that can reduce healing time and improve the treatment. Nanocrystalline silver contains antibacterial and anti-inflammatory properties. This study aimed to evaluate the efficacy of Agicoat in the treatment of partial-thickness skin graft donor sites of burn patients in terms of healing time, pain and scarring. METHOD: , Mepitel and Vaseline gauze. On days 4 and 8, the amount of pain when changing the dressing was recorded based on visual analog scale (VAS). After six months, the patients were evaluated and compared for the scarring site based on Vancouver Scar Scale (VSS). RESULT: Comparison of the average healing time between groups showed that the average healing time in both groups was significantly shorter than the Vaseline group (P=0.005). Comparison of wound pain between groups on Day 4 showed that the mean pain in the Agicoat group and also the Mepitel group was significantly lower than the Vaseline group (P=0.004). However, Agicoat and Mepitel groups did not show a significant difference. Also, a comparison of pain between groups on Day 8 and the mean VAS six months after skin graft showed no difference between groups. CONCLUSION: According to the findings of this study, if the Agicoat dressing is cost-effective, it can be a good alternative to cover the wound of the skin donor site, and it heals faster and reduces pain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".