Interventions in the management of diabetes‐related foot infections: A systematic review
Bibliographic record
Abstract
The optimal approaches to managing diabetic foot infections remain a challenge for clinicians. Despite an exponential rise in publications investigating different treatment strategies, the various agents studied generally produce comparable results, and high-quality data are scarce. In this systematic review, we searched the medical literature using the PubMed and Embase databases for published studies on the treatment of diabetic foot infections from 30 June 2018 to 30 June 2022. We combined this search with our previous literature search of a systematic review performed in 2020, in which the infection committee of the International Working Group on the Diabetic Foot searched the literature until June 2018. We defined the context of the literature by formulating clinical questions of interest, then developing structured clinical questions (Patients-Intervention-Control-Outcomes) to address these. We only included data from controlled studies of an intervention to prevent or cure a diabetic foot infection. Two independent reviewers selected articles for inclusion and then assessed their relevant outcomes and methodological quality. Our literature search identified a total of 5,418 articles, of which we selected 32 for full-text review. Overall, the newly available studies we identified since 2018 do not significantly modify the body of the 2020 statements for the interventions in the management of diabetes-related foot infections. The recent data confirm that outcomes in patients treated with the different antibiotic regimens for both skin and soft tissue infection and osteomyelitis of the diabetes-related foot are broadly equivalent across studies, with a few exceptions (tigecycline not non-inferior to ertapenem [±vancomycin]). The newly available data suggest that antibiotic therapy following surgical debridement for moderate or severe infections could be reduced to 10 days and to 3 weeks for osteomyelitis following surgical debridement of bone. Similar outcomes were reported in studies comparing primarily surgical and predominantly antibiotic treatment strategies in selected patients with diabetic foot osteomyelitis. There is insufficient high-quality evidence to assess the effect of various recent adjunctive therapies, such as cold plasma for infected foot ulcers and bioactive glass for osteomyelitis. Our updated systematic review confirms a trend to a better quality of the most recent trials and the need for further well-designed trials to produce higher quality evidence to underpin our recommendations.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".