Assessment Of Prevalence And Risk Factors Of Urinary Tract Infections In Pediatric Diabetic Cases: A Systematic Review
Bibliographic record
Abstract
Background Urinary tract infections (UTIs) are among the most common bacterial infections affecting individuals with diabetes mellitus. Pediatric patients with diabetes are particularly vulnerable due to immature immune defenses and metabolic instability. Understanding the prevalence and risk factors for UTIs in this group is crucial for prevention and management. Objective To systematically review and synthesize current empirical literature on the prevalence, risk factors, microbial patterns, and antibiotic resistance of UTIs among pediatric and adolescent diabetic populations. Methods This study followed PRISMA 2020 guidelines for systematic reviews. Databases including PubMed, Scopus, Embase, and Google Scholar were searched for studies published between 2010 and 2025. Eligibility criteria included peer-reviewed studies on UTI prevalence and associated risk factors in pediatric or diabetic populations. Twenty-two studies were included after rigorous screening and quality assessment using the Newcastle-Ottawa Scale and AMSTAR-2. Results The review identified a consistently elevated prevalence of UTIs in diabetic children, with female sex, poor glycemic control, chronic kidney disease, and residual urine volume as key risk factors. A notable rise in antibiotic resistance was observed, particularly among common pathogens such as E. coli. Glycosuria, medication effects (e.g., SGLT2 inhibitors), and structural urinary tract abnormalities further increased susceptibility. Conclusion Pediatric patients with diabetes are at significantly elevated risk for UTIs, requiring targeted diagnostic protocols and tailored antimicrobial strategies. Early identification of high-risk individuals, especially females and those with poor metabolic control, can reduce complications. Further pediatric-specific longitudinal research is warranted.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".