The development of international wound debridement best practice recommendations: Consensus between nurses specialized in Wound, Ostomy and Continence Canada and the society of tissue viability
Bibliographic record
Abstract
Debridement is an important component of wound management and can improve outcomes for patients. Debridement needs to be done by an appropriately trained health professional, but the scope of practice, credentials, training, competencies, and regulatory requirements regarding wound debridement can differ. Best Practice Recommendations were created to positively influence patient safety related to all methods of debridement, across the continuum of care, and to be implemented widely by nurses at all professional levels in Canada. AIM: To further develop the Best Practice Recommendations for wound debridement, with an international perspective, by creating a consensus document to support the global adoption of evidence-based debridement practice for health professionals. METHODS: A consensus meeting utilising Delphi methods was conducted between the authors to review the consensus statements. Once 80 % agreement was achieved, a wide range of wound care experts were identified by the authors and invited to participate in an external review of the statements. RESULTS: Fifteen consensus statements about wound debridement were agreed upon and are presented in this paper. CONCLUSIONS: These best practice recommendations have been reviewed by a wide range of practitioners from across the UK and Canada and aim to provide guidance on the standardisation of debridement practices for healthcare professionals.
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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 | 0.209 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".