Cosmetic results of wound treatment using the living skin equivalent in open tibial fractures
Bibliographic record
Abstract
BACKGROUND: Soft tissue wound treatment in open tibial fractures requires complex clinical approach. New cellular methods of wound treatment must be compared with the gold standard split-thickness skin grafting. AIM: To compare the esthetic results of wound management with the living skin equivalent and skin grafting. MATERIAL AND METHODS: A comparative study included 108 patients with open tibial fractures and soft tissue defects who underwent staged surgical treatment. In group 1 (n=51), the living skin equivalent was used, which is a bioengineered three-layer construction containing keratinocytes, fibroblasts, and collagen matrix. In group 2 (n=57), standard split-thickness skin grafting was used for wound repair. The surgery duration, complete epithelialization period, hospital stay, Vancouver Scar Scale (VSS) after 3, 6, and 12 months, and self-reported esthetic results 1 year after surgery were compared. RESULTS: Living skin equivalent procedures were performed significantly faster than skin grafting (18.24.8 min vs. 35.514.8 min, р 0.001), and wound healing took longer (25.56.3 days vs. 19.64.7 days, р=0.035). The overall VSS score was significantly lower at all follow-up visits in group 1 than in group 2 (6.230.81 points vs. 8.120.98 points after 3 months, р 0.001; 5.171.18 points vs. 6.761.31 points after 6 months, р 0.001; 4.541.07 points vs. 5.090.65 points after 12 months, р=0.038). Moreover, 74.5 and 68.4% of the patients were satisfied with the appearance of the limb after treatment with living skin equivalent and skin grafting, respectively (р=0.023). CONCLUSION: The cosmetic results of wound treatment in open tibial fractures with living skin equivalents are significantly better than those of split-thickness skin grafting.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".