Effectiveness of a monofilament wound debridement pad at removing biofilm and slough:<i>ex vivo</i>and clinical performance
Bibliographic record
Abstract
Objective: Removal of slough and other devitalised tissue is an important step in biofilm-based wound care (BBWC) and wound bed preparation. Debridement is key to management of both slough and biofilm, and a number of methods are available to achieve this, including surgical/sharp and mechanical debridement. Developments have led to products indicated for debridement of wounds, including a sterile pad consisting of monofilament fibres. Our aim is to examine the effectiveness of a monofilament wound debridement pad (WDP), Debrisoft. Method: We assessed the WDP, in laboratory tests, for the removal of mature biofilm from porcine dermal tissue in an ex vivo model, and the clinical management of sloughy wounds that would benefit from debridement. We used the UPPER score to determine the superficial infection status. Results: The WDP was effective in removing biofilm from porcine dermal tissue. A case series of 10 patients with chronic wounds suggested that the WDP was beneficial in the removal of slough. All chronic wounds had slough and were cleaned weekly, for four weeks, using the MDP to achieve improved healing and a clean wound bed. The average wound size decreased from 8.09cm2at baseline to 2.3cm2at week four, with three wounds healed completely. Exudate was reduced, and the UPPER score improved in every patient. Conclusion: These results indicate that the WDP effectively debrides biofilm and slough, and contributes to care that follows the principles of wound bed preparation and BBWC.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".