Early-onset Sepsis Among Preterm Neonates in China, 2015 to 2018
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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