Epidemiology of Meningitis in Canadian Neonatal Intensive Care Units
Bibliographic record
Abstract
BACKGROUND: Meningitis is a serious disease that occurs more commonly in the neonatal period than in any other age group. Recent data from large national cohorts are needed to determine if the epidemiology of neonatal meningitis (NM) has changed. AIM: To assess the rates, causative organisms, risk factors, temporal trends and short-term outcomes of NM in Canadian Neonatal Intensive Care Units (NICUs). METHODS: A retrospective review of newborn infants admitted to NICUs participating in the Canadian Neonatal Network between January 2010 and December 2016. Patients with meningitis were reviewed. Outcomes of patients with meningitis were compared with 1:2 matched (for gestation, sex and birth weight) neonates without meningitis. RESULTS: Rates of NM ranged between 2.2 and 3.5/1000 NICU admissions during the 7-year study period with the majority of patients (87%) having late-onset meningitis (at >3 days after birth). The most common bacterial organism for both early- and late-onset meningitis was Escherichia coli followed by group B streptococci. Only 31% [95% confidence interval (CI): 24.06-38.63) of neonates with meningitis had simultaneous bacteremia. NM was associated with increased seizures [odds ratio (OR): 8.63; 95% CI: 4.73-15.7], retinopathy of prematurity (OR: 3.23; 95% CI: 1.30-8.02), bronchopulmonary dysplasia (OR: 1.93; 95% CI: 1.11-3.35), days of mechanical ventilation (OR: 1.03; 95% CI: 1.02-1.04) and length of hospital stay (OR: 1.02; 95% CI: 1.01-1.02), but not with mortality before discharge (OR: 1.29; 95% CI: 0.74-2.23). CONCLUSIONS: The rate of NM remains largely unchanged in Canadian NICUs. NM was associated with increased major morbidities and longer hospital stay but not with mortality before discharge.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".