Optimizing Burn Injury Care: A Comparative Network Meta-Analysis of Skin Grafts' Efficacy
Bibliographic record
Abstract
Burn injuries, causing approximately 180,000 deaths. Skin grafting serves as a cornerstone intervention in burn injury management. This study aimed to evaluate the efficacy of different types of skin grafts in promoting the healing process of burn injuries. The outcomes of the study were the reepithelialization time and repigmentation rate of skin graft. Quality appraisal was done using ROBINS-I and the Newcastle Ottawa Scale, while network and single arm meta-analysis were conducted using R-Studio. A literature search across 6 databases resulted in the selection of 7 articles. Quality assessment categorized 6 studies as low- and 1 as moderate-risk of bias studies. The device facilitated autologous skin cell increase in reepithelialization rate (OR=5.21; 95% CI=0.24-114.41), followed by autologous skin cell and synthetic graft (OR=0.47; 95% CI=0.04-5.98), topical agent (OR=0.30; 95% CI=0.06-1.47), and Xenograft (OR=0.09; 95% CI=0.00-4.89). Furthermore, the single-arm meta-analysis showed an overall repigmentation rate of 61.49%, with xenograft displaying the highest repigmentation rate (87.50%), followed by synthetic graft (70.32%), and autologous skin cell (37.54%). Xenografts have proven effective in promoting burn wound healing. Nevertheless, the outstanding efficacy and minimal repigmentation of device-facilitated autologous skin cell transplantation highlight its potential in clinical practice.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".