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Record W4406051482 · doi:10.1542/peds.2024-068200

Optimizing Management of Febrile Young Infants Without Serum Procalcitonin

2025· article· en· W4406051482 on OpenAlexaff
Brett Burstein, Caroline Wolek, Cassandra Poirier, Alexandra Yannopoulos, T. Charles Casper, Mohammed Kaouache, Nathan Kuppermann

Bibliographic record

VenuePEDIATRICS · 2025
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsUniversity of British ColumbiaMcGill UniversityConcordia UniversityMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsMedicineProcalcitoninIntensive care medicinePediatricsInternal medicineSepsis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.287
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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