The Use of Acellular Dermal Matrix in Release of Burn Contracture Scars in the Hand
Bibliographic record
Abstract
BACKGROUND: Contracture deformities of the upper extremity are encountered frequently in burn victims. Surgical repair of this problem is challenged by a paucity of soft tissue, poor functional outcome, and a high rate of recurrence. Acellular dermal matrix has become increasingly popular in reconstructive surgery--at times--as an alternative to local and free tissue transfer in different parts of the body. However, its applicability in contracture release, particularly in hand surgery, has not been widely explored. METHODS: Nine patients with burn contracture scars involving different locations in the hand and the wrist underwent two-stage reconstruction consisting of contracture release and use of acellular dermal matrix followed by definitive coverage with skin graft at the second stage. Patients were followed up for a period of at least 10 months (range, 10 to 25 months), during which time the passive range of motion of the hand was used as a quantitative measure of surgical outcome. RESULTS: All nine patients retained at least 83 percent of the corrected range of motion involving the affected joints by 1 year and at least 89 percent of correction at each webspace. No patient required a revision procedure. CONCLUSION: Acellular dermal matrix can be an effective tool in surgical treatment of difficult burn contracture deformity in the hand, with lasting results.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".