What is the optimal dressing for securing a split-thickness skin graft? A systematic review and meta-analysis of the studies comparing NPWT with traditional dressings
Bibliographic record
Abstract
BackroundSplit-thickness skin grafts (STSG) are commonly employed for repairing significant skin defects. Traditional aftercare typically involves applying a protective layer of petroleum and cotton gauze dressing. Nonetheless, this conventional approach to securing and protecting the skin graft is frequently cumbersome and not very effective. Negative-pressure wound therapy (NPWT) has emerged as a promising alternative method for dressing split-thickness grafts, offering a more efficient solution.MethodsA systematic review of studies comparing STSG dressing methods was performed. The Medline/Pubmed, Embase, Scopus, Cochrane Library, and Web of Science databases were thoroughly searched. The data concerning graft take percentage, reoperation rate, and complication (infection, seroma, hematoma) rates were extracted. The Cochrane risk-of-bias tool was used for randomized studies, while Newcastle-Ottawa Quality Assessment for Cohort Studies was used for other types of research. Statistical analysis was conducted with PS IMAGE PRO 9.0 (IBM SPSS Statistics 29.0) software.ResultsOur meta-analysis included 29 studies (14 randomized and 15 non-randomized), comprising a total of 2,025 patients. No study was excluded because of its poor quality. We observed a significantly higher percentage of graft take in the group of patients treated with NPWT when compared to those treated with conventional therapy (standardized mean difference (SMD) = 1,34% (95% CI: 0,62-2,07%; p<0,01)). NPWT was associated with a reduction in reoperation rate (OR = 0,29 (95% CI: 0,17-0,50; p<0,01)), a reduction in graft infection rate (OR = 0,37 (95% CI: 0,24-0,58; p<0,01)), and a reduction in seroma formation rate (OR = 0,38 (95% CI: 0,15-0,93; p = 0,03)). The reduction in hematoma formation rate was not statistically significant (OR = 0,64 (95% CI: 0,33-1,22; p = 0,17)).ConclusionsWhen contrasted with traditional treatment methods, negative-pressure wound therapy (NPWT) notably enhances the success rate of grafting and lowers the need for additional surgeries when utilized for split-thickness skin grafting. Additionally, NPWT decreases the risk of wound infection and seroma formation under the graft, which improves the healing process of the graft’s recipient site.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".