Wound Bed Preparation 2024: Delphi Consensus on Foot Ulcer Management in Resource-Limited Settings
Bibliographic record
Abstract
ABSTRACT GENERAL PURPOSE To review a practical and scientifically sound application of the wound bed preparation model for communities without ideal resources. TARGET AUDIENCE This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and registered nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES After participating in this educational activity, the participant will: 1. Summarize issues related to wound assessment. 2. Identify a class of drugs for the treatment of type II diabetes mellitus that has been shown to improve glycemia, nephroprotection, and cardiovascular outcomes. 3. Synthesize strategies for wound management, including treatment in resource-limited settings. 4. Specify the target time for edge advancement in chronic, healable wounds. BACKGROUND Chronic wound management in low-resource settings deserves special attention. Rural or underresourced settings (ie, those with limited basic needs/healthcare supplies and inconsistent availability of interprofessional team members) may not have the capacity to apply or duplicate best practices from urban or abundantly-resourced settings. OBJECTIVE The authors linked world expertise to develop a practical and scientifically sound application of the wound bed preparation model for communities without ideal resources. METHODS A group of 41 wound experts from 15 countries reached a consensus on wound bed preparation in resource-limited settings. RESULTS Each statement of 10 key concepts (32 substatements) reached more than 88% consensus. CONCLUSIONS The consensus statements and rationales can guide clinical practice and research for practitioners in low-resource settings. These concepts should prompt ongoing innovation to improve patient outcomes and healthcare system efficiency for all persons with foot ulcers, especially persons with diabetes.
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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 | 0.267 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".