Interventions in the management of infection in the foot in diabetes: 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 as of June 2018. This systematic review is an update of previous reviews, the first of which was undertaken in 2010 and the most recent in 2014, by the infection committee of the International Working Group of the Diabetic Foot. We defined the context of literature by formulating clinical questions of interest, then developing structured clinical questions (PICOs) 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 the methodological quality. Our literature search identified a total of 15 327 articles, of which we selected 48 for full-text review; we added five more studies discovered by means other than the systematic literature search. Among these selected articles were 11 high-quality studies published in the last 4 years and two Cochrane systematic reviews. Overall, the outcomes in patients treated with the different antibiotic regimens for both skin and soft tissue infection and osteomyelitis of the diabetic foot were broadly equivalent across studies, except that treatment with tigecycline was inferior to ertapenem (±vancomycin). Similar outcomes were also 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 adjunctive therapies, such as negative pressure wound therapy, topical ointments or hyperbaric oxygen, on infection related outcomes of the diabetic foot. In general, the quality of more recent trial designs are better in past years, but there is still a great 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 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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".