Our initial experience with rapid enzymatic debriding agent for burn eschar: Case series from an ABA verified burn center
Bibliographic record
Abstract
• NexoBrid achieved ≥95% eschar removal in all 14 patients with one application, proving effective non-surgical debridement. • 43% of patients avoided skin grafting entirely; for those needing it, grafted area averaged 63% of treated burn area, preserving viable dermis. • NexoBrid may reduce surgical intervention, aid wound healing, and enhance scar quality, especially for high-risk surgical patients. We reviewed 14 consecutive patients at our ABA-verified burn center who received enzymatic debridement with anacaulase-bcdb (NexoBrid®) from January 2020 to May 2023. These patients, part of the NEXT study, had deep partial or full-thickness burns. We aimed to evaluate NexoBrid’s effect on eschar removal, wound healing, surgical needs, and scar quality. Data included total body surface area (TBSA) burned, enzymatically treated area, amount of NexoBrid used, grafting details, time to healing, and scar characteristics. Analysis was descriptive, reporting medians, ranges, and percentages. All 14 patients achieved ≥ 95 % eschar removal with a single NexoBrid application. Their ages ranged from 15–65 years, and mean burn size was 9.25 % TBSA. Eight patients required grafting, but these grafts covered only about 60 % of the treated area. Time to 95 % wound closure averaged 36 days. Scar assessment using the Vancouver Scar Scale showed improvement from a mean score of 3.8 at three months to 0.5 at twelve months. Despite this, four patients developed hypertrophic scars and one required intervention for a contracture. In summary, NexoBrid facilitated rapid, consistent non-surgical eschar removal, timely wound closure, and favorable scar outcomes within one year. In nearly half of the patients, it eliminated the need for skin grafting. Among those who did require grafts, smaller graft areas were needed. These findings suggest that early eschar removal and dermal preservation contribute to improved outcomes. Further studies with larger cohorts will help confirm these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".