Early-onset Sepsis Among Preterm Neonates in China, 2015 to 2018
Bibliographic record
Abstract
BACKGROUND: The epidemiology of early-onset sepsis (EOS) in China is poorly understood because of the paucity of high-quality data. We aimed to examine the epidemiology, pathogen distribution and neonatal outcomes of EOS among a large cohort of preterm infants in China. METHODS: All infants born at <34 weeks of gestation and admitted to 25 tertiary neonatal intensive care units in China from April 2015 to May 2018 were enrolled. EOS was defined as a culture-confirmed infection that occurred within 72 hours after birth. RESULTS: Among 27,532 enrolled infants, 321 (11.7 cases per 1000 admissions) infants developed EOS, and 61 (19.0%) infants died within seven days after EOS onset. The incidence of EOS among inborn infants in 18 perinatal centers was 9.7 cases per 1000 live births <34 weeks' gestation (186/19,084). The case fatality rate was 22.6% (42/186). Gram-negative bacteria were responsible for 61.7% of EOS and 82.0% of EOS-related deaths. Escherichia coli (20.3%) was the leading pathogen, followed by Coagulase-negative staphylococcus (16.5%), Achromobacter xylosoxidans (9.0%) and Klebsiella pneumoniae (8.1%). Group B streptococci infections were relatively rare (2.5%). EOS was an independent risk factor for all-cause mortality and retinopathy of prematurity. CONCLUSIONS: There is a high burden of EOS among preterm infants in China with a distinctive pathogen distribution. Longitudinal epidemiologic monitoring, further investigation of causative pathogens and development of targeted strategies for prevention and treatment of EOS are needed.
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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 | 0.001 | 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".