Consensus guidelines for the identification and treatment of biofilms in chronic nonhealing wounds
Bibliographic record
Abstract
BACKGROUND: Despite a growing consensus that biofilms contribute to a delay in the healing of chronic wounds, conflicting evidence pertaining to their identification and management can lead to uncertainty regarding treatment. This, in part, has been driven by reliance on in vitro data or animal models, which may not directly correlate to clinical evidence on the importance of biofilms. Limited data presented in human studies have further contributed to the uncertainty. Guidelines for care of chronic wounds with a focus on biofilms are needed to help aid the identification and management of biofilms, providing a clinical focus to support clinicians in improving patient care through evidence-based medicine. METHODS: A Global Wound Biofilm Expert Panel, comprising 10 clinicians and researchers with expertise in laboratory and clinical aspects of biofilms, was identified and convened. A modified Delphi process, based on published scientific data and expert opinion, was used to develop consensus statements that could help identify and treat biofilms as part of the management of chronic nonhealing wounds. Using an electronic survey, panel members rated their agreement with statements about biofilm identification and treatment, and the management of chronic nonhealing wounds. Final consensus statements were agreed on in a face-to-face meeting. RESULTS: Participants reached consensus on 61 statements in the following topic areas: understanding biofilms and the problems they cause clinicians; current diagnostic options; clinical indicators of biofilms; future options for diagnostic tests; treatment strategies; mechanical debridement; topical antiseptics; screening antibiofilm agents; and levels of evidence when choosing antibiofilm treatments. CONCLUSION: This consensus document attempts to clarify misunderstandings about the role of biofilms in clinical practice, and provides a basis for clinicians to recognize biofilms in chronic nonhealing wounds and manage patients optimally. A new paradigm for wound care, based on a stepped-down treatment approach, was derived from the consensus statements.
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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 | 0.163 | 0.273 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".