Epidemiology and microbiology of late-onset sepsis among preterm infants in China, 2015–2018: A cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it