Enzymatic debridement with collagenase in wounds and ulcers: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Enzymatic debridement with collagenase is a technique that is commonly used in clinical practice. This systematic review examines the effect of collagenase on all kinds of wounds, compared to an alternative therapy, on wound healing, wound bed characteristics, cost‐effectiveness and the occurrence of adverse events. We conducted a systematic literature search on available literature in Cochrane databases, MEDLINE, EMBASE and CINAHL. Two investigators independently assessed the titles and abstracts of all randomised controlled trials obtained involving collagenase of all kinds of wounds based on inclusion criteria. Of the 1411 citations retrieved, 22 studies reported outcomes with the use of collagenase either for wound healing or wound debridement. Results support the use of collagenase for enzymatic debridement in pressure ulcers, diabetic foot ulcers and in conjunction with topical antibiotics for burns. However, studies presented a high risk of bias. Risk ratio of developing an adverse event related to collagenase versus the alternative treatment was statistically significant (for 10 studies, RR: 1·79, 95% CI 1·24–2·59, I2=0%, P = 0·002). There is very limited data on the effect of collagenase as an enzymatic debridement technique on wounds. More independant research and adequate reporting of adverse events are warranted.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".