Bromelain-Based Enzymatic Debridement Versus Standard of Care in Deep Burn Injuries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Deep burn injuries necessitate effective debridement to promote healing and reduce complications. Traditional surgical debridement is the standard of care; however, it can lead to significant tissue loss, excessive bleeding and delayed healing. Bromelain-based enzymatic debridement offers a potential less invasive alternative that aims to selectively remove necrotic tissue while preserving viable ones. Therefore, this systematic review and meta-analysis comprehensively compares bromelain debridement versus standard care in the management of partial and full thickness burns. Cochrane Library, Embase, and Medline were searched until May 30, 2024 for studies comparing bromelain debridement versus standard care. R version 4.4.0 was used to pooled risk ratio and mean difference in a random-effects model. We included 7 studies, comprising 484 participants, of whom 238 (49%) were treated with enzymatic debridement. Bromelain significantly reduced time to eschar removal (MD -7.60 days 95% CI [-9.76, -5.44]; I2 = 70%) in comparison with standard care. Additionally, bromelain group presented a significant reduction in the risk of surgical excision (RR 0.17; 95% CI [0.06, 0.47]; I2 = 79%) and need for autografts (RR 0.40; 95% CI [0.18, 0.93]; I2 = 76%) in comparison with standard group. No differences were found in behalf of time to wound closure (MD -7.64 days; 95% CI [-18.46]-[3.18]; I2 = 86%), nor in Modified Vancouver Scar Scale (MD -0.36 points; 95% CI [-0.96]-[0.23]; I2 = 0%). Bromelain-based enzymatic debridement may accelerate eschar removal and reduce the need for surgical excision and autografts, without adversely affecting wound closure time or long-term scar quality.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".