Early-onset sepsis in very preterm neonates in Australia and New Zealand, 2007–2018
Bibliographic record
Abstract
Objective To evaluate the epidemiology and population trends of early-onset sepsis in very preterm neonates admitted to neonatal intensive care units (NICU) in Australia and New Zealand. Design Retrospective observational cohort study using a dual-nation registry database. Setting 29 NICUs that have contributed to the Australian and New Zealand Neonatal Network. Participants Neonates born at <32 weeks’ gestation born between 2007 and 2018 and then admitted to a NICU. Main outcome measures Microorganism profiles, incidence, mortality and morbidity. Results Over the 12-year period, 614 early-onset sepsis cases from 43 178 very preterm admissions (14.2/1000 admissions) were identified. The trends of early-onset sepsis incidence remained stable, varying between 9.8 and 19.4/1000 admissions (linear trend, p=0.56). The leading causative organisms were Escherichia coli ( E. coli ) (33.7%) followed by group B Streptococcus ( GBS ) (16.1%). The incidence of E. coli increased between 2007 (3.2/1000 admissions) and 2018 (8.3/1000 admissions; p=0.02). Neonates with E. coli had higher odds of mortality compared with those with GBS (OR=2.8, 95% CI 1.2 to 6.1). Mortality due to GBS decreased over the same period (2007: 0.6/1000 admissions, 2018: 0.0/1000 admissions; p=0.01). Early-onset sepsis tripled the odds of mortality (OR=3.0, 95% CI 2.4 to 3.7) and halved the odds of survival without morbidity (OR=0.5, 95% CI 0.4 to 0.6). Conclusion Early-onset sepsis remains an important condition among very preterm populations. Furthermore, E. coli is a dominant microorganism of very preterm early-onset sepsis in Australia and New Zealand. Rates of E. coli have been increasing in recent years, while GBS -associated mortality has decreased.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".