Comparison of the three dressing methods on the speed of repair and remaining scar on partial-thickness skin graft donor sites in burn patients
Bibliographic record
Abstract
Background: Rapid repair of skin donor sites in partial-thickness skin grafts in burn is critical to the patient. Severe pain during dressing change and scarring also reduce the quality of life and treatment. Recently, nano-silver particles are available which have a high surface to volume ratio and remain effective even at a very low concentration and minimize the chance for tissue toxicity due to silver. Our aim in this study was to evaluate the effect of nano-silver dressings on increasing the rate of repair and reduction of complications in non-infectious wounds of skin graft donor sites. Methods: This was comparison study that involved patients admitted to a single-center burn unit who required a skin graft donor site. 10 burn patients with thermal injury and 10-30% of total body surface area (TBSA) who referred to Shahid Motahari Hospital, Tehran, in 2016 were studied. Each patient was compared to herself. After anesthesia, the site of the skin graft was randomly divided into three sections in each patient and was covered with Agicoat®, Mepitel and vaseline gauze. On the 4th and 8th of the day, the pain was recorded during the dressing change on the basis of the visual analogue scale (VAS). After 6 months, the patients were evaluated for the remainder of the scars based on VSS (Vancouver scar scale). Results: The mean time to repair in both groups were similar and significantly shorter than that of vaseline (P=0.005). The pain comparison between groups showed that on the fourth day, the mean pain in the Agicoat group and Mepitel was significantly less than the Vaseline group (P=0.004). Also, the mean pain of the epileptic group was significantly lower than that of the Vaseline group (0.002). However, there was no significant difference between the duration of regeneration and pain between the two groups of Agicoat and Mepitel, but there was no significant difference between the mean pain on the 8th day and the mean VAS 6 months after the graft. Conclusion: Agicoat dressing has been effective in reducing pain and increasing the repair speed, but this effect was similar to that of the silver-free Mepitel dressing. Expensive silver nano-crystal dressings limit their use. The silver nanocrystal did not have much effect on improving the remaining scar. However, more studies are needed in this regard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".