Incidence of Bacteremia and Serious Bacterial Infections in Hyperpyrexic Infants Offered Universal Pneumococcal Conjugate Vaccine 13 and Haemophilus influenzae B Immunization
Bibliographic record
Abstract
BACKGROUND: High fevers, especially in young children, often alarm clinicians and prompt extensive evaluation based on perceptions of increased risk of serious bacterial infection (SBI), and even brain damage or seizure disorders. OBJECTIVE: The aim of this study was to determine the prevalence of SBI in infants aged 3-36 months with fever ≥40.5°C in a population of infants offered universal pneumococcal conjugate vaccine 13 and Haemophilus influenzae B immunization. METHODS: This study is a retrospective review of all infants aged 3-36 months with temperature ≥40.5°C presenting to a tertiary care pediatric emergency department over a 30-month period in an era of universal pneumococcal conjugate 13 and H. influenzae B immunization. RESULTS: SBI was recorded in 54 (21.8%) of 247 study infants, most commonly pneumonia 30 patients (12.1%) and urinary tract infection 16 patients (6.5%). Two patients had positive blood cultures, yielding a bacteremia rate of 0.8%. Patients with SBI had a significantly higher WBC count ( P < 0.0001) and C-reactive protein levels ( P < 0.0001), and were significantly more likely to be hospitalized ( P < 0.0001). DISCUSSION: Although SBI was common (21.8%) in our cohort of hyperpyrexic infants universally offered vaccination with pneumococcal conjugate 13 and H. influenzae B vaccines, bacteremia was a rare finding (0.8%).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".