IWGDF/IDSA Guidelines on the Diagnosis and Treatment of Diabetes-related Foot Infections (IWGDF/IDSA 2023)
Bibliographic record
Abstract
The International Working Group on the Diabetic Foot (IWGDF) has published evidence-based guidelines on the management and prevention of diabetes-related foot diseases since 1999. The present guideline is an update of the 2019 IWGDF guideline on the diagnosis and management of foot infections in persons with diabetes mellitus. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework was used for the development of this guideline. This was structured around identifying clinically relevant questions in the P(A)ICO format, determining patient-important outcomes, systematically reviewing the evidence, assessing the certainty of the evidence, and finally moving from evidence to the recommendation. This guideline was developed for healthcare professionals involved in diabetes-related foot care to inform clinical care around patient-important outcomes. Two systematic reviews from 2019 were updated to inform this guideline, and a total of 149 studies (62 new) meeting inclusion criteria were identified from the updated search and incorporated in this guideline. Updated recommendations are derived from these systematic reviews, and best practice statements made where evidence was not available. Evidence was weighed in light of benefits and harms to arrive at a recommendation. The certainty of the evidence for some recommendations was modified in this update with a more refined application of the GRADE framework centred around patient important outcomes. This is highlighted in the rationale section of this update. A note is also made where the newly identified evidence did not alter the strength or certainty of evidence for previous recommendations. The recommendations presented here continue to cover various aspects of diagnosing soft tissue and bone infections, including the classification scheme for diagnosing infection and its severity. Guidance on how to collect microbiological samples, and how to process them to identify causative pathogens, is also outlined. Finally, we present the approach to treating foot infections in persons with diabetes, including selecting appropriate empiric and definitive antimicrobial therapy for soft tissue and bone infections; when and how to approach surgical treatment; and which adjunctive treatments may or may not affect the infectious outcomes of diabetes-related foot problems. We believe that following these recommendations will help healthcare professionals provide better care for persons with diabetes and foot infections, prevent the number of foot and limb amputations, and reduce the patient and healthcare burden of diabetes-related foot disease.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".