Does Extreme Leukocytosis Predict Serious Bacterial Infections in Infants in the Post-Pneumococcal Vaccine Era? The Experience of a Large, Tertiary Care Pediatric Hospital
Bibliographic record
Abstract
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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".