The microbiology of diabetic foot infections: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Diabetic foot ulcers are a common complication of poorly controlled diabetes and often become infected, termed diabetic foot infection. There have been numerous studies of the microbiology of diabetic foot infection but no meta-analysis has provided a global overview of these data. This meta-analysis aimed to investigate the prevalence of bacteria isolated from diabetic foot infections using studies of any design which reported diabetic foot infection culture results. METHODS: The Medline, EMBASE, Web of Science and BIOSIS electronic databases were searched for studies published up to 2019 which contained microbiological culture results from at least 10 diabetic foot infection patients. Two authors independently assessed study eligibility and extracted the data. The main outcome was the prevalence of each bacterial genera or species. RESULTS: = 93.8% [93.0-94.5%]) was MRSA. Other highly prevalent organisms were Pseudomonas spp., E. coli and Enterococcus spp. A correlation was identified between Gross National Income and the prevalence of Gram positive or negative organisms in diabetic foot infections. CONCLUSION: The microbiology of diabetic foot infections is diverse, but S. aureus predominates. The correlation between the prevalence of Gram positive and negative organisms and Gross National Income could reflect differences in healthcare provision and sanitation. This meta-analysis has synthesised multiple datasets to provide a global overview of the microbiology of diabetic foot infections that will help direct the development of novel therapeutics.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.039 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".