Epidemiology, microbiology and antibiotic treatment of bacterial and fungal meningitis among very preterm infants in China: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Neonatal meningitis significantly contributes to neonatal morbidity and mortality, yet large-scale epidemiological data in developing countries, particularly among very preterm infants (VPIs), remain sparse. This study aimed to describe the epidemiology of meningitis among VPIs in China. DESIGN: Cross-sectional study using the Chinese Neonatal Network database from 2019 to 2021. SETTING: 79 tertiary neonatal intensive care units in China. PATIENTS: Infants with gestational age <32 weeks or birth weight <1500 g. MAIN OUTCOME MEASURES: Incidence, pathogen distribution, antimicrobial use and outcomes of bacterial and fungal meningitis. RESULTS: Of 31 915 VPIs admitted, 122 (0.38%) infants were diagnosed with culture-confirmed meningitis, with 14 (11.5%) being early-onset (≤6 days of age) and 108 (88.5%) being late-onset (>6 days of age). The overall in-hospital mortality was 18.0% (22/122). A total of 127 pathogens were identified, among which 63.8% (81/127) were Gram-negative bacteria, 24.4% (31/127) were Gram-positive bacteria and 11.8% (15/127) were fungi. In terms of empirical therapy (on the day of the first lumbar puncture), the most commonly used antibiotic was meropenem (54.9%, 67/122). For definitive therapy (on the sixth day following the first lumbar puncture, 86 cases with available antibiotic data), meropenem (60.3%, 35/58) and vancomycin (57.1%, 16/28) were the most used antibiotics for Gram-negative and Gram-positive bacterial meningitis, respectively. 44% of infants with Gram-positive bacterial meningitis and 52% with Gram-negative bacterial meningitis received antibiotics for more than 3 weeks. CONCLUSION: 0.38% of VPIs in Chinese neonatal intensive care units were diagnosed with meningitis, experiencing significant mortality and inappropriate antibiotic therapy. Gram-negative bacteria were the predominant pathogens, with fungi emerging as a significant cause.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".