Limited debridement combined with ReCell® Techniques for deep second-degree burns
Bibliographic record
Abstract
BACKGROUND: The purpose of this article is to introduce a method that combines limited debridement and ReCell® autologous cell regeneration techniques for the treatment of deep second-degree burn wounds. METHOD: A total of 20 patients suffered with deep second-degree burns less than 10% of total body surface area (TBSA) who were admitted to our department, from June 2019 to June 2021, participated in this study. These patients first underwent limited debridement with an electric/pneumatic dermatome, followed by the ReCell® technique for secondary wounds. Routine treatment was applied to prevent scarring after the wound healed. Clinical outcomes were scored using the Vancouver Scar Scale (VSS). RESULTS: All wounds of the patients healed completely. One patient developed an infection in the skin graft area and finally recovered by routine dressing changes. The average healing time was 12 days (range: 10-15 days). The new skin in the treated area was soft and matched the colour of the surrounding normal skin and the VSS score ranged from 3~5 for each patient. Of the 20 patients, 19 were very satisfied and 1 was satisfied. CONCLUSIONS: This article reports a useful treatment method that combines electric dermatome-dependent limited debridement and the ReCell® technique for the treatment of deep second-degree burn wounds. It is a feasible and effective strategy that is easy to implement and minimally invasive, and it is associated with a short healing time, mild scar formation and little damage to the donor skin area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".