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Record W2307904817 · doi:10.1097/pec.0000000000000454

Does Extreme Leukocytosis Predict Serious Bacterial Infections in Infants in the Post-Pneumococcal Vaccine Era? The Experience of a Large, Tertiary Care Pediatric Hospital

2015· article· en· W2307904817 on OpenAlexaff
Ayelet Rimon, Dennis Scolnik, Galia Grisaru‐Soen, Miguel Glatstein

Bibliographic record

VenuePediatric Emergency Care · 2015
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsLeukocytosisMedicineBacteremiaWhite blood cellPneumoniaChest radiographEtiologyPediatricsInternal medicineBlood cultureUrinary systemIntensive care medicineImmunologyLungAntibiotics

Abstract

fetched live from OpenAlex

BACKGROUND: Extreme leukocytosis, defined as a peripheral white blood cell count greater than 25,000/mm, may alarm clinicians and prompt extensive evaluation in infants with fever, especially in the pediatric emergency department. METHODS: We reviewed data from children aged 3 to 36 months with extreme leukocytosis, fever and the risk of serious bacterial infections (SBI) at our institution from July 2010 to December 2012, a period after the universal introduction of pneumococcal vaccine. RESULTS: Serious bacterial infection was recorded in 57 (39%) of the 147 infants. The most common SBI were segmental or lobar pneumonia, in 28 (19%) patients, and urinary tract infection in 16 (10.9%) patients. Three patients had positive blood cultures, corresponding to a bacteremia rate of 2%. C-reactive protein was significantly higher in the SBI group than in the non-SBI group. CONCLUSIONS: All well-looking febrile infants with white blood cell greater than 25,000/mm should undergo a chest radiograph unless there are clear physical findings that indicate a different etiology. Urine culture should be considered in women. C-reactive protein can have an added value in the differential diagnosis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.264
Teacher spread0.255 · 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 teacher head, 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

Citations10
Published2015
Admission routes1
Has abstractyes

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