Meek Micro-Skin Grafting and Acellular Dermal Matrix in Pediatric Patients: A Novel Approach to Massive Extravasation Injury
Bibliographic record
Abstract
(1) Background: Extravasation injuries in pediatric patients can lead to significant harm if they are not promptly diagnosed and treated. However, evidence-based standardization on extravasation management remains limited, particularly for extensive wound necrosis. This case report presents the management of an 8-week-old premature patient with an extensive extravasation injury involving the right forearm and dorsum of the hand. (2) Methods: The patient was evaluated by a multidisciplinary team in our Neonatal Intensive Care Unit. Surgical intervention involved the debridement of necrotic tissues, followed by temporary coverage with an acellular dermal matrix. Definitive coverage was achieved through Meek micrografting after three weeks. Physical therapy was provided with pre- and post-rehabilitation range of motion assessed using goniometric measurements. Scar quality was evaluated using the Vancouver Scar Scale. (3) Results: The engraftment rate of the Meek micrografts was 93%, with 16 out of 226 micrografts lost. The patient achieved a Vancouver Scar Scale score of 6, indicating a moderate degree of scarring. Significant improvements in elbow, wrist, and finger joint range of motion were observed at a 1-year follow-up. (4) Conclusions: Close observation and heightened awareness of extravasation risks by trained personnel are crucial. Meek micrografting combined with dermal substitute coverage represents an innovative approach to managing extravasation wounds in pediatric patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".