Autologous Skin Grafts, versus Tissue-engineered Skin Constructs: A Systematic Review and Meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it