Early Enzymatic Burn Debridement: Results of the DETECT Multicenter Randomized Controlled Trial
Bibliographic record
Abstract
Since 1970 surgeons have managed deep burns by surgical debridement and autografting. We tested the hypothesis that enzymatic debridement with NexoBrid would remove the eschar reducing surgery and achieve comparable long-term outcomes as standard of care (SOC). In this Phase 3 trial, we randomly assigned adults with deep burns (covering 3-30% of total body surface area [TBSA]) to NexoBrid, surgical or nonsurgical SOC, or placebo Gel Vehicle (GV) in a 3:3:1 ratio. The primary endpoint was complete eschar removal (ER) at the end of the debridement phase. Secondary outcomes were need for surgery, time to complete ER, and blood loss. Safety endpoints included wound closure and 12 and 24-months cosmesis on the Modified Vancouver Scar Scale. Patients were randomized to NexoBrid (n = 75), SOC (n = 75), and GV (n = 25). Complete ER was higher in the NexoBrid versus the GV group (93% vs 4%; P < .001). Surgical excision was lower in the NexoBrid vs the SOC group (4% vs 72%; P < .001). Median time to ER was 1.0 and 3.8 days for the NexoBrid and SOC respectively (P < .001). ER blood loss was lower in the NexoBrid than the SOC group (14 ± 512 mL vs 814 ± 1020 mL, respectively; P < .0001). MVSS scores at 12 and 24 months were noninferior in the NexoBrid versus SOC groups (3.7 ± 2.1 vs 5.0 ± 3.1 for the 12 months and 3.04 ± 2.2 vs 3.30 ± 2.76 for the 24 months). NexoBrid resulted in early complete ER in >90% of burn patients, reduced surgery and blood loss. NexoBrid was safe and well tolerated without deleterious effects on wound closure and scarring.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".