Bacterial profile and antimicrobial resistance patterns of infected diabetic foot ulcers in sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract The number of diabetic foot ulcer patients is substantially increasing, with the rapidly rising burden of diabetic mellitus in sub-Saharan Africa. The data on the regional prevalence of diabetic foot ulcer infecting bacteria and their antimicrobial resistance patterns is crucial for its proper management. This systematic review and meta-analysis determined the pooled prevalence of bacterial profiles and antimicrobial resistance patterns of infected diabetic foot ulcers in sub-Saharan Africa. A comprehensive search of the literature was performed on CINAHL, EMBASE, Google Scholar, PubMed, Scopus, and Web of Science databases. Critical appraisal was done using the Joanna Briggs Institute’s tool for prevalence studies. A pooled statistical meta-analysis was conducted using STATA Version 17.0. The I 2 statistics and Egger’s test were used to assess the heterogeneity and publication bias. The pooled prevalence and the corresponding 95% confidence interval of bacterial profiles and their antimicrobial resistance patterns were estimated using a random effect model. Eleven studies with a total of 1174 study participants and 1701 bacteria isolates were included. The pooled prevalence of the most common bacterial isolates obtained from DFU were S. aureus (34.34%), E. coli (21.16%), and P. aeruginosa (20.98%). The highest pooled resistance pattern of S. aureus was towards Gentamicin (57.96%) and Ciprofloxacin (52.45%). E.coli and K. Pneumoniae showed more than a 50% resistance rate for the most common antibiotics tested. Both gram-positive and gram-negative bacteria were associated with diabetic foot ulcers in sub-Saharan Africa. Our findings are important for planning treatment with the appropriate antibiotics in the region. The high antimicrobial resistance prevalence rate indicates the need for context-specific effective strategies aimed at infection prevention and evidence-based alternative therapies.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".