Epidemiology and microbiology of late-onset sepsis among preterm infants in China, 2015–2018: A cohort study
Bibliographic record
Abstract
OBJECTIVE: To describe the incidence, case-fatality rate and pathogen distribution of late-onset sepsis (LOS) among preterm infants in China. To investigate risk factors and short-term outcomes associated with LOS caused by Gram-positive bacteria, Gram-negative bacteria and fungi. METHODS: This cohort study included all infants born at <34 weeks' gestation and admitted to 25 tertiary hospitals in 19 provinces in China from May, 2015 to April, 2018. Infants were excluded who died or were discharged within 3 days of being born. RESULTS: A total of 1199 episodes of culture-positive LOS were identified in 1133 infants, with an incidence of 4.4% (1133/25,725). Overall, 15.4% (175/1133) of infants with LOS died and 10.0% (113/1133) of infants died within 7 days of LOS onset. Among 1214 isolated pathogens, Gram-negative bacteria were the most common (51.8%, 629/1214) and fungi accounted for 17.1% (207/1214). Use of central lines, longer duration of antibiotics and previous carbapenem exposure were related to increased risk of fungal LOS compared with Gram-positive bacteria. Gram-negative bacteria LOS was independently associated with increased risk of death, periventricular leukomalacia, bronchopulmonary dysplasia, and necrotizing enterocolitis. Fungal LOS was independently associated with increased risk of periventricular leukomalacia, bronchopulmonary dysplasia and necrotizing enterocolitis. CONCLUSIONS: Late-onset sepsis was a significant cause of morbidity and mortality in Chinese neonatal intensive care units, with a distinct pathogen distribution from industrial countries. Clinical guidelines on the prevention and treatment of LOS should be developed and tailored to these LOS characteristics in Chinese neonatal intensive care units.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".