Late-Onset Sepsis among Extremely Preterm Infants of 24–28 Weeks Gestation: An International Comparison in 10 High-Income Countries
Bibliographic record
Abstract
INTRODUCTION: Despite advances in neonatal care, late-onset sepsis remains an important cause of preventable morbidity and mortality. Neonatal late-onset sepsis rates have decreased in some countries, while in others they have not. Our objective was to compare trends in late-onset sepsis rates in 9 population-based networks from 10 countries and to assess the associated mortality within 7 days of late-onset sepsis. METHODS: We performed a retrospective population-based cohort study. Infants born at 24-28 weeks' gestation between 2007 and 2019 were eligible for inclusion. Late-onset sepsis was defined as a positive blood or cerebrospinal fluid culture. Late-onset sepsis rates were calculated for 3 epochs (2007-11, 2012-15, and 2016-19). Adjusted risk ratios (aRRs) for late-onset sepsis were calculated for each network. RESULTS: Of a total of 82,850 infants, 16,914 (20.4%) had late-onset sepsis, with Japan having the lowest rate (7.1%) and Spain the highest (44.6%). Late-onset sepsis rates decreased in most networks and remained unchanged in a few. Israel, Sweden, and Finland showed the largest decrease in late-onset sepsis rates. The aRRs for late-onset sepsis showed wide variations between networks. The rate of mortality temporally related to late-onset sepsis was 10.9%. The adjusted mean length of stay for infants with late-onset sepsis was increased by 5-18 days compared to infants with no late-onset sepsis. CONCLUSIONS: One in 5 neonates of 24-28 weeks' gestation develops late-onset sepsis. Wide variability in late-onset sepsis rates exists between networks with most networks exhibiting improvement. Late-onset sepsis was associated with increased mortality and length of stay.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".