Febrile Young Infants With Altered Urinalysis at Low Risk for Invasive Bacterial Infection. A Spanish Pediatric Emergency Research Network’s Study
Bibliographic record
Abstract
BACKGROUND: Urinary tract infection (UTI) is the most common serious bacterial infection (SBI) in infants younger than 90 days of age. Many physicians admit infants younger than 90 days old because of their greater risk of developing invasive bacterial infections (IBIs), secondary to UTI. The primary objective of this study was to design a prediction model to identify febrile infants younger than 90 days old with an altered urinalysis who were at low risk for IBI and suitable for outpatient management METHODS: : Prospective multicenter study included 19 hospitals that are members of the Spanish Pediatric Emergency Research Group of the Spanish Society of Pediatric Emergencies. Febrile infants younger than 90 days old with altered urinalysis were included. RESULTS: A total of 766 (22.5%) infants with altered urine dipstick were analyzed. Fifty (6.5%) of them developed IBI, 39 (78.0%) secondary to UTI. Patients were at low risk for IBI if they were well appearing at arrival to the emergency department, were older than 21 days and had procalcitonin and C-reactive protein (CRP) blood values lower than 0.5 ng/mL and 20 mg/L, respectively. These factors were used to create a prediction model for IBI secondary to UTI, with a sensitivity of 100% (95% CI: 89.3-100) and a negative predictive value of 100% (95% CI: 97.5-100). CONCLUSIONS: We have derived a highly accurate prediction model for IBI in febrile infants with altered urinalysis. Given these results, outpatient management might be suitable for 1 of each 4 infants diagnosed, with a considerable improvement in resource utilization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".