Efficiency of Bromelain-Enriched Enzyme Mixture (NexoBrid™) in the Treatment of Burn Wounds
Bibliographic record
Abstract
Background: The use of bromelain for the removal of eschar in deep burns is considered to be effective because it does not affect the unaffected skin and leaves a clean dermis after use. The main objective of this study is to find out whether bromelain is a good alternative to surgical debridement. In order to achieve that, we aim to evaluate its indications, limitations, and safety measures. Methods: The current study was conducted on a group of 30 patients with deep burn lesions, aged 20 to 56 years, from which 15 underwent enzymatic debridement and 15 patients acted as a control group in which primary surgical debridement was used. The mixture of enzymes enriched in bromelain, meant to dissolve burn eschar, was provided by NexoBrid™. The inclusion criteria were in agreement with the manufacturer’s protocols, but the application protocol was slightly modified in order to implement a better intern protocol and to assess its efficiency. Results: Complete eschar debridement was obtained in 13 of the 15 cases, from which 10 patients went through spontaneous healing and 3 needed to be covered with a skin graft. In the other 2 cases, partial eschar debridement was associated with surgical debridement and coverage with split-thickness skin graft in the same operation. The results obtained in the two groups were assessed with the Vancouver Scar Scale. Conclusions: Even though early excision followed by coverage with split-thickness skin graft remains the gold standard for the treatment of deep burns, enzymatic debridement can provide a series of advantages when the inclusion and exclusion criteria are respected. Bromelain is an alternative to surgical debridement that provides speed, tissue selectivity, safety, and less blood loss.
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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 | 0.001 |
| 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".