The Epidemiology, Management, and Outcomes of Bacterial Meningitis in Infants
Bibliographic record
Abstract
OBJECTIVES: The pathogens that cause bacterial meningitis in infants and their antimicrobial susceptibilities may have changed in this era of increasing antimicrobial resistance, use of conjugated vaccines, and maternal antibiotic prophylaxis for group B Streptococcus (GBS). The objective was to determine the optimal empirical antibiotics for bacterial meningitis in early infancy. METHODS: This was a cohort study of infants <90 days of age with bacterial meningitis at 7 pediatric tertiary care hospitals across Canada in 2013 and 2014. RESULTS: There were 113 patients diagnosed with proven meningitis (n = 63) or suspected meningitis (n = 50) presented at median 19 days of age, with 63 patients (56%) presenting a diagnosis from home. Predominant pathogens were Escherichia coli (n = 37; 33%) and GBS (n = 35; 31%). Two of 15 patients presenting meningitis on day 0 to 6 had isolates resistant to both ampicillin and gentamicin (E coli and Haemophilus influenzae type B). Six of 60 infants presenting a diagnosis of meningitis from home from day 7 to 90 had isolates, for which cefotaxime would be a poor choice (Listeria monocytogenes [n = 3], Enterobacter cloacae, Cronobacter sakazakii, and Pseudomonas stutzeri). Sequelae were documented in 84 infants (74%), including 8 deaths (7%). CONCLUSIONS: E coli and GBS remain the most common causes of bacterial meningitis in the first 90 days of life. For empirical therapy of suspected bacterial meningitis, one should consider a third-generation cephalosporin (plus ampicillin for at least the first month), potentially substituting a carbapenem for the cephalosporin if there is evidence for Gram-negative meningitis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".