Antimicrobial Vs Non-Antimicrobial Dressings for Neuropathic Plantar Diabetic Foot Ulcers Under Standardized Offloading: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Neuropathic plantar diabetic foot ulcers (DFUs) represent a major clinical problem due to their high risk of infection, prolonged morbidity, and frequent progression to amputation. Although standardized offloading remains the cornerstone of management, the role of antimicrobial dressings in improving outcomes compared with non-antimicrobial moist dressings remains controversial. This study aimed to evaluate the efficacy of antimicrobial dressings in enhancing ulcer healing and reducing adverse outcomes in patients with neuropathic plantar DFUs managed under standardized offloading. A systematic review and meta-analysis was conducted following PRISMA guidelines. PubMed, Embase, Cochrane Library, and ClinicalTrials.gov were searched through August 2025. Eligible studies included randomized controlled trials (RCTs) and observational studies comparing antimicrobial dressings (silver, PHMB, honey, iodine) with non-antimicrobial moist dressings. Outcomes assessed were complete healing at 12–16 weeks, amputation rates, and antibiotic use. Data were pooled using random-effects models, and risk of bias was assessed with Cochrane RoB 2.0 and the Newcastle-Ottawa Scale. Fifteen studies (eight RCTs, seven observational) met the inclusion criteria. Pooled results indicated antimicrobial dressings improved healing rates (OR ? 2.1, 95% CI 1.5–3.0), with silver dressings showing modest benefits in infection-related outcomes. However, no consistent effect on amputation rates or antibiotic duration was observed, and the largest RCT (2023) showed no significant benefit over standard moist dressings. Antimicrobial dressings may provide selective benefit in severe or infected ulcers but lack consistent superiority for routine use. These findings suggest that clinicians should adopt a targeted, adjunctive approach while further multicenter RCTs clarify subgroup effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".