Sprayed Cultured Epithelial Autografts for Deep Dermal Burns of the Face and Neck
Bibliographic record
Abstract
In Brief The objective of this study was the assessment of clinical results after sprayed application of cultured epithelial autograft (CEA) suspensions onto deep dermal burn wounds of the face and neck. Nineteen patients with deep dermal burns of the face and neck were included into a prospective study. The average total body surface area burn was 15.1% (7%–46%; median: 13%). The average Abbreviated Burn Severity Index (ABSI) was 6.7 points (4–12 points; median: 7 points). The application of sprayed CEA suspension was performed onto an average body surface area of 2% (0.5–5%; median: 2%). Thirteen patients were recruited for clinical follow-up after an average of 10 months (3–18 months). The average Vancouver Scar Scale score at follow-up was 2.4 ± 2.2 points (range, 0–8 points), and the average Donnersmarck and Hörbrand score was 9.3 ± 6.8 points (range, 0–22). Four patients had less than 9 months’ follow-up. Excluding these patients from the analysis resulted in an average Vancouver Scar Scale score of 1.3 ± 0.9 points (range, 0–3 points) and an average Donnersmarck and Hörbrand score of 8.0 ± 7.4 points (range 0–22) for the remaining 9 patients. Our data show that enzymatic and careful surgical debridement and consecutive application of CEA suspensions using a spray technique results in excellent cosmetic outcomes compared with any other method. Thirteen patients with deep dermal burns of the face and neck were treated with enzymatic and surgical debridement, followed by the topical spraying of cultured epithelial autograft suspensions over a median 2% of TBSA. Scar quality was found to be superior to other methods of resurfacing.
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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 | 0.000 | 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".