60: Short-Term and Long-Term Outcomes of Neonatal Gram-Negative Sepsis in Canadian NICUs
Bibliographic record
Abstract
Gram-negative sepsis (GNS) is associated with increased neonatal mortality and morbidity compared with Gram-positive and fungal sepsis. Limited research has examined the differences in short-term and long-term outcomes of early-onset and late-onset GNS. To compare short-term and long-term outcomes of neonates with early-onset and late-onset GNS with those who did not develop sepsis in Canadian NICUs. A retrospective cohort study of neonates GA ≤29 weeks admitted between April 1, 2009 and September 30, 2011 was conducted using data from the Canadian Neonatal Network (CNN) and Canadian Neonatal Follow-Up Network (CNFUN) databases. Demographics and rates of mortality, BPD, ROP, NEC, and IVH were compared between three groups: a) early-onset GNS (EOS), b) late-onset GNS (LOS) and c) those with no sepsis. Follow-up metrics and composite neurodevelopmental impairment (NDI) indices at 18–21 months CGA were compared between the three groups. 2182 infants met inclusion criteria; 29 (1.5%) developed EOS and 171 (7.8%) LOS. Baseline comparison revealed lower GA (P<0.01), birth weight (P<0.01), Apgar scores at 1 and 5 minutes (P<0.01) and higher SNAP II scores (P<0.01), higher rate of chorioamnionitis (P<0.01), and longer duration of stay (P<0.01) among those who developed GNS. E. coli and Citrobacter were more common among EOS, while LOS was associated with Enterobacter, Enterococcus, Klebsiella, and other gram-negative organisms (P<0.01). NEC was more common in neonates with LOS (AOR [95% CI]: 3.81 [2.14, 6.79]). GNS in Canadian NICUs is associated with an increased rate of NEC and increased length of hospital stay. There was no difference in neurodevelopmental impairment rates between groups when adjusted for confounders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".