Optimizing Management of Febrile Young Infants Without Serum Procalcitonin
Bibliographic record
Abstract
BACKGROUND: Febrile young infants are at risk of invasive bacterial infections (IBIs; bacteremia or bacterial meningitis). American Academy of Pediatrics (AAP) guidelines recommend that when procalcitonin testing is unavailable, C-reactive protein (CRP), absolute neutrophil count (ANC) and temperature should be used to identify low-risk infants. We sought to determine the optimal combination of these inflammatory markers to predict IBI when procalcitonin is unavailable. METHODS: This was a secondary analysis of prospectively collected data for all febrile infants aged 60 days or younger evaluated at a tertiary pediatric emergency department (January 2018 to July 2023). Previously healthy term infants aged 8 to 60 days with rectal temperatures of 38.0°C or greater meeting AAP inclusion/exclusion criteria were analyzed. A decision rule was derived by classification and regression tree analysis with 10-fold cross-validation then compared to AAP-recommended thresholds of ANC ≤ 5200/mm3, CRP ≤ 20 mg/L, and temperature ≤ 38.5°C. RESULTS: Among 1987 infants, 38 (1.9%) had IBIs. The AAP-recommended thresholds missed no IBIs (sensitivity: 100.0% [95% CI, 88.6%-100.0%]; negative predictive value (NPV): 100.0% [95% CI, 99.5%-100.0%]; specificity: 50.7% [95% CI, 48.5%-53.0%]). Optimal derived thresholds were CRP ≤ 22.2mg/L, temperature ≤ 39.0°C, and ANC ≤ 4500/mm3; urinalysis and age were not selected. The derived rule also missed no IBIs (sensitivity: 100.0% [95% CI, 88.6%-100.0%]; NPV: 100.0% [95% CI, 99.7%-100.0%]); however, specificity improved to 83.8% (95% CI, 82.1%-85.4%). Area under the receiver operating curve for the cross-validated rule (91.9% [95% CI, 91.1%-92.7%]) was higher than at AAP-recommended thresholds (75.4% (95% CI, 74.3%-76.5%]). CONCLUSIONS: The combination of ANC, CRP, and temperature at statistically derived thresholds improved diagnostic accuracy for identifying infants at low risk of IBIs compared to AAP-recommended thresholds.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".