Antibiotic Resistance Patterns of Uropathogenic Causes of Urinary Tract Infections in < 3-Year-Old Children: A Single-Center Cross-Sectional Study
Bibliographic record
Abstract
Background: Urinary tract infections (UTIs) are among the most common childhood infections and can lead to serious complications, such as hypertension and renal failure, if not diagnosed and treated promptly. The prompt initiation of appropriate empiric therapy in children with upper UTIs requires the identification of causative bacteria and their antibiotic resistance patterns. Objectives: The aim of this study was to investigate the frequency and patterns of antibiotic resistance among uropathogenic bacteria causing UTIs in children under 3 years of age admitted to the 17th Shahrivar Hospital in Rasht, Iran. Methods: A total of 259 children diagnosed with UTIs from 2014 to 2020 were admitted to our hospital and included in the study. The age, sex, clinical symptoms, urine analysis results, urine culture findings, and antibiogram of the patients were documented in a questionnaire. The data were then analyzed in SPSS software version 21. Results: The mean age of the children was 4.9 ± 2.7 months. Boys comprised 53.3% of the patients. Escherichia coli was the most frequent cause of UTIs in the children (56.4%), followed by Klebsiella (33.2%). The highest resistance was related to cephalothin (77.1 %), cephalexin (77.1 %), ampicillin (78.8 %), and amoxicillin (100 %). Conclusions: The most common uropathogenic bacterium causing UTIs in young children was E. coli, which showed sensitivity to ciprofloxacin, amikacin, gentamicin, nitrofurantoin, imipenem, ceftriaxone, and nalidixic acid. Accordingly, it is advisable to use aminoglycosides as the drugs of choice to treat UTIs in children under the age of 3 years. In the case of any contraindication, third-generation cephalosporins are recommended for empirical treatment, and if there is no response to these treatments within 48 to 72 hours, ciprofloxacin can be considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".