Treatment Delay of Febrile Urinary Tract Infections Among Infants With Respiratory Symptoms
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether antibiotic treatment of febrile urinary tract infection (UTI) is delayed in febrile infants with respiratory symptoms compared with those without. STUDY DESIGN: Data of infants 2-24 months of age diagnosed with UTI from March 1, 2012 to May 31, 2023 were collected from our hospital's medical charts and triage records. Patients with known congenital anomalies of the kidney and urinary tract or a history of febrile UTI were excluded. Patients were classified as having respiratory symptoms if they had any of the following symptoms or clinical signs: cough, rhinorrhea, pharyngeal hyperemia and otitis media. Time to first antibiotic treatment from fever onset was compared between patients with and without respiratory symptoms. A Cox regression model was constructed to adjust for potential confounders. RESULTS: A total of 214 patients were eligible for analysis. The median age of the eligible patients was 5.0 months (interquartile range: 3.0-8.8) and 118 (55%) were male. There were 104 and 110 patients in the respiratory symptom and no respiratory symptom groups, respectively. The time to first antibiotic treatment was significantly longer in the group with respiratory symptoms (51 hours vs. 21 hours). Respiratory symptoms were significantly associated with a longer time to first treatment after adjustment for age and sex in the Cox regression model (hazard ratio = 0.63, 95% confidence interval: 0.47-0.84). CONCLUSIONS: Treatment of febrile UTI infants with respiratory symptoms tends to be delayed. Pediatricians should not exclude febrile UTI even in the presence of respiratory symptoms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".