714 Our Experience with Enzymatic Debriding Agent, Anacaulase, for Burn Injuries: A 14 Patient Case Series
Bibliographic record
Abstract
Abstract Introduction Background: Enzymatic debridement of burn wounds facilitates early removal of eschar. It has shown to improve healing time and decrease need for surgical intervention. We wanted to review our results of use of an enzymatic debriding agent (EDA - Anacaulase) in deep partial and full thickness burns. Methods: Methods We undertook a retrospective review of consecutive 14 adult patients treated with EDA at our Burn Center. These patients received therapy with Anacaulase, a novel enzymatic debriding agent, spanning from the year 2020 to the present day. Burn characteristics, EDA applications details, surgical interventions if any, time of heal, scar assessments were noted. We used Microsoft Excel for a descriptive analysis of the data. We examined continuous variables within specific ranges and provided summaries including median, minimum, maximum values, and percentages. Results: Results Patients demographics and burn injury characteristics are included in table 1. We were able to achieve >95% eschar removal with single application in all 14 patients. 57% patients required subsequent skin grafts. The time to achieve 95% wound closure averaged 35.5 days. Scars improved substantially over the study period, as indicated by mean Vancouver Scar Scale scores of 3.8 to 0.5 at 3 and 12 months, respectively. Only 2 patients required scar release surgery due to contracture. Conclusions Conclusion: Our review highlights potential benefits of early eschar removal with EDA. 47% patients were able to avoid autologous skin graft closure of deep partial and full thickness burn wounds. EDA (Anacaulase) may add value in care of select patients with deep partial and full thickness burn wounds. Applicability of Research to Practice Our review highlights potential benefits of early eschar removal with EDA. EDA (Anacaulase) may add value in care of select patients with deep partial and full thickness burn wounds.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".