Split thickness skin graft versus application of the temporary skin substitute suprathel in the treatment of deep dermal hand burns: a retrospective cohort study of scar elasticity and perfusion.
Bibliographic record
Abstract
Two therapeutic options for deep dermal hand burns are autologous split-thickness skin graft (STSG) following tangential excision and the application of the temporary wound dressing Suprathel following removal of burn blisters. We compared elasticity and perfusion of burn scars after both types of therapy at least one year after completion of treatment. A case series of 80 patients of our department with deep dermal hand burns between 2013 and 2018 was examined in the year 2019 at least one year after completion of treatment (24 females and 56 males with a median age of 47.6 years). The clinical assessment of the scar was performed with the Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS) and the objective assessment with suction cutometry (MPA 580) and an O2C device on both hands. Our statistical analysis showed no statistically significant differences for the R2 and R5 elasticity values between the two types of therapy. The 95% confidence intervals for the ratios of elasticity, and microcirculatory perfusion parameters and scar scale scores of burn scars to respective healthy areas of skin after STSG and Suprathel-therapy mostly covered 1. Subgroup analysis of R2 viscoelasticity and analyses with adjustments for scar compression therapy, nicotine consumption, age, palmar or dorsal localization of the burn scar and interactions of age with smoking and localization gave similar results. The adjusted analysis of SO2 showed statistically significant lower SO2 values, 9% less, after STSG compared to Suprathel treatment. Split-thickness skin graft following tangential excision and the application of Suprathel following removal of burn blisters may be equivalent options for treatment of deep dermal hand burns. To detect possible small differences, further studies with larger samples are required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".