Functional and aesthetic reconstruction of digital flexion contractures with full‐thickness plantar skin grafts in children
Bibliographic record
Abstract
Hand burns are frequently seen in children, often resulting in digital flexion contractures. Traditional split-thickness or full-thickness skin grafts leave notably different skin texture and hyperpigmentation. The purpose of this study was to describe our operation for treating digital flexion contractures with full-thickness plantar skin grafts, and to evaluate the appearance and function outcomes. Hematoxylin and eosin staining, Masson trichrome staining and Melan A (marker of melanocyte) staining were used to evaluate palmar skin, plantar skin, groin skin and burn scars. Full-thickness plantar skin grafts were performed between 2008 and 2015 in 24 hand burn patients with digital flexion contracture. The average age at the time of surgery was 39.3 months and the average follow-up period was 5.5 years. The functional and cosmetic results were assessed. Plantar skin shared similar attributes with palmar skin histologically. Both plantar skin and palmar skin did not express melan A. All of the skin grafts survived well without hematoma, infection and necrosis. The grafts resembled the adjacent normal skin in regards to appearance and texture. The average TAM (total active movement) degree for the fingers was improved from 152.3° to 238.5°. The average VSS (Vancouver Scar Scale) score decreased dramatically from 10.4 to 1.1. Twenty one of twenty four patients (21/24, 87.5%) were very satisfied with function and appearance, and three in twenty four (3/24, 12.5%) were somewhat satisfied. This study indicates that full-thickness plantar skin grafts can achieve a satisfactory appearance and good function for hand burn child patients with digital flexion contractures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".