Autologous Skin Grafts, versus Tissue-engineered Skin Constructs: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
For over 100 years, autologous skin grafts have remained the gold standard for the reconstruction of wounds but are limited in availability. Acellular tissue-engineered skin constructs (acellular TCs) and cellular tissue-engineered skin constructs (cellular TCs) may address these limitations. This systematic review and meta-analysis compare outcomes between them. Methods: A systematic review was conducted using PRISMA guidelines, querying MEDLINE, Embase, Web of Science, and Cochrane to assess graft incorporation, failure, and wound healing. Case reports/series, reviews, in vitro/in vivo work, non-English articles or articles without full text were excluded. Results: Sixty-six articles encompassing 4076 patients were included. No significant differences were found between graft failure rates (P = 0.07) and mean difference of percent reepithelialization (p = 0.92) when split-thickness skin grafts were applied alone versus co-grafted with acellular TCs. Similar mean Vancouver Scar Scale was found for these two groups (p = 0.09). Twenty-one studies used at least one cellular TC. Weighted averages from pooled results did not reveal statistically significant differences in mean reepithelialization or failure rates for epidermal cellular TCs compared with split-thickness skin grafts (p = 0.55). Conclusions: This systematic review is the first to illustrate comparable functional and wound healing outcomes between split-thickness skin grafts alone and those co-grafted with acellular TCs. The use of cellular TCs seems promising from preliminary findings. However, these results are limited in clinical applicability due to the heterogeneity of study data, and further level 1 evidence is required to determine the safety and efficacy of these constructs.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".