The role of human skin allograft in minimising hypertrophic scaring in burn wound healing: A meta-analytical study
Bibliographic record
Abstract
Hypertrophic scarring is a common and debilitating complication of burn wound healing, often leading to functional impairment and poor cosmetic outcomes. Human skin allografts have been increasingly used as a biological dressing to optimize wound repair and potentially reduce abnormal scar formation. This meta-analytical study aimed to evaluate the effectiveness of human skin allografts in minimizing hypertrophic scarring in burn wound healing compared to conventional treatment methods. A systematic search of PubMed, Scopus, Web of Science, and Cochrane Library databases was conducted for studies published between 2000 and 2023. Sixteen eligible studies involving burn patients treated with human skin allografts versus standard care were included. Data were extracted on scar outcomes, and statistical analysis was performed using a random-effects model. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, and heterogeneity was assessed using the I² statistic. Risk of bias was evaluated using Cochrane and Newcastle-Ottawa tools. All 16 studies reported lower rates of hypertrophic scarring in patients treated with allografts. The pooled analysis revealed a significant reduction in scar formation with allografts compared to controls (OR = 0.31, 95% CI: 0.20-0.48, p<0.001). Heterogeneity across studies was minimal (I² ≈ 0%), suggesting consistency of findings. Risk of bias was low to moderate, and no included study reported outcomes favouring control treatment. Human skin allografts significantly reduce the risk of hypertrophic scarring in burn wound healing by providing rapid wound coverage, reducing inflammation, and promoting organized tissue repair. The findings support their broader integration into burn management protocols, although further large-scale randomized controlled trials are recommended to strengthen the evidence base.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".