The Efficacy of Bromelain-based Enzymatic Debridement in Deep Burn Injury Management: A Comprehensive Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objectives: Bromelain-based enzymatic debridement has gained significant attention for its potential advantages in treating burn wounds. However, no previous meta-analyses have been conducted to provide concise evidence of bromelain's efficacy. Therefore, this study aims to conduct a systematic review and meta-analysis focusing on the comparison of bromelain use versus standard care in patients undergoing burn debridement. Methods: Systematic searches were conducted in PubMed, Embase, and the Cochrane Library databases to identify relevant studies. Data synthesis was performed using random-effects models, with findings presented as mean differences (MDs) and weighted odds ratios, accompanied by their corresponding 95% confidence intervals. Results: The review analyzed 10 studies, including three randomized controlled trials, two clinical trials, and four observational studies, involving 596 burn patients with an average age of 38 years and a mean total body surface area (TBSA) of 16%. Of the participants, 49% received a topical bromelain mixture, while 51% received standard care. Bromelain was linked to faster debridement (MD -3.92; p < 0.00001) and a higher rate of spontaneous healing (MD 71.00; p < 0.00001). Surgical excision and autograft procedures were less common in the bromelain group, with odds ratios of 0.08 (p < 0.0002) and 0.10 (p = 0.0001). No significant differences were observed in wound closure time, infection rates, blood transfusion needs, Modified Vancouver Scar Scale scores, or hospital stays. Conclusions: The evidence from this study suggests that the administration of bromelain-based enzymatic debridement significantly reduces the time required to complete debridement, increases the chances of spontaneous healing of burn wounds, and decreases the need for further surgical excisions and autograft procedures.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".