Comparison between artificial dermis with split‐thickness skin graft and full‐thickness skin graft for reconstruction of joint‐involved burn wounds: A retrospective review from a tertiary burn centre
Bibliographic record
Abstract
Abstract We aimed to compare the scar quality and recovery rate of joint activity for patients with joint‐involved burn injuries receiving either artificial dermis (AD) with split‐thickness skin graft (STSG) or full‐thickness skin graft (FTSG) for reconstruction. The primary outcomes were %skin graft (SG) take. Secondary outcomes included complications such as the infection rate and donor site morbidity, 12‐month scar quality evaluated using the Vancouver scar scale (VSS), recovery rate of joint activity and incidence of scar contracture requiring further revision. Twenty‐eight patients between 1 August 2021, and 1 August 2023, were enrolled. Twelve patients received AD‐STSG while the other 16 patients underwent FTSG for reconstruction. The median %SG take was 95.0% (interquartile range [IQR] 6.3%) and 96.0% (IQR 10.0%) for the AD‐STSG and FTSG groups ( p = 0.71). The FTSG group had significantly better 12‐month scar quality (median VSS 4.0 [IQR 1.3] vs. 6.0 [IQR1.5], p < 0.01) and recovery rate of joint activity (median 82.5% [IQT 15.0%] vs. 70.0% [IQR 7.5%], p < 0.01) compared with AD‐STSG group. However, two patients in the FTSG group (12.5%) suffered partial wound dehiscence of the donor site, whereas no patients experienced donor site morbidity in the AD‐STSG group ( p = 0.49). The incidence of scar contracture requiring further revision was 25.0% (3/12) in the AD‐STSG group and 12.5% (2/16) in the FTSG group ( p = 0.62). In conclusion, AD‐STSG could be an alternative treatment over FTSG for larger joint‐involved burn wounds (>200 cm 2 ) owing to lesser donor site morbidity with admissible cosmetic outcomes and functional recovery.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".