Febrile Infants With Urinary Tract Infections at Very Low Risk for Adverse Events and Bacteremia
Bibliographic record
Abstract
BACKGROUND: There is limited evidence from which to derive guidelines for the management of febrile infants aged 29 to 60 days with urinary tract infections (UTIs). Most such infants are hospitalized for ≥48 hours. Our objective was to derive clinical prediction models to identify febrile infants with UTIs at very low risk of adverse events and bacteremia in a large sample of patients. METHODS: This study was a 20-center retrospective review of infants aged 29 to 60 days with temperatures of ≥38°C and culture-proven UTIs. We defined UTI by growth of ≥50,000 colony-forming units (CFU)/mL of a single pathogen or ≥10,000 CFU/mL in association with positive urinalyses. We defined adverse events as death, shock, bacterial meningitis, ICU admission need for ventilator support, or other substantial complications. We performed binary recursive partitioning analyses to derive prediction models. RESULTS: We analyzed 1895 patients. Adverse events occurred in 51 of 1842 (2.8% [95% confidence interval (CI): 2.1%-3.6%)] and bacteremia in 123 of 1877 (6.5% [95% CI: 5.5%-7.7%]). Patients were at very low risk for adverse events if not clinically ill on emergency department (ED) examination and did not have a high-risk past medical history (prediction model sensitivity: 98.0% [95% CI: 88.2%-99.9%]). Patients were at lower risk for bacteremia if they were not clinically ill on ED examination, did not have a high-risk past medical history, had a peripheral band count of <1250 cells per μL, and had a peripheral absolute neutrophil count of ≥1500 cells per μL (sensitivity 77.2% [95% CI: 68.6%-84.1%]). CONCLUSION: Brief hospitalization or outpatient management with close follow-up may be considered for infants with UTIs at very low risk of adverse events.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".