Factors Associated with Survival and Survival without Major Morbidity in Very Preterm Infants in Two Neonatal Networks: SEN1500 and NEOCOSUR
Bibliographic record
Abstract
INTRODUCTION: Very low-birth weight (VLBW) infants represent a high-risk population for morbidity and mortality in the neonatal period. Variability in practices and outcomes between centers has been acknowledged. Multicenter benchmarking studies are useful to detect areas of improvement and constitute an interesting research tool. OBJECTIVES: The aim of the study was to determine the perinatal variables and interventions associated with survival and survival without major morbidity in VLBW infants and compare the performance of 2 large networks. METHODS: This is a prospective study analyzing data collected in 2 databases, the Spanish SEN1500 and the South American NEOCOSUR networks, from January 2013 to December 2016. Inborn patients, from 240 to 306 weeks of gestational age (GA) were included. Hazard ratios for survival and survival without major morbidity until the first hospital discharge or transfer to another facility were studied by using Cox proportional hazards regression. RESULTS: A total of 10,565 patients, 6,120 (57.9%) from SEN1500 and 4,445 (42.1%) from NEOCOSUR, respectively, were included. In addition to GA, birth weight, small for gestational age (SGA), female sex, and multiple gestation, less invasive resuscitation, and the network of origin were significant independent factors influencing survival (aHR [SEN1500 vs. NEOCOSUR]: 1.20 [95% CI: 1.15-1.26] and survival without major morbidity: 1.34 [95% CI: 1.26-1.43]). Great variability in outcomes between centers was also found within each network. CONCLUSIONS: After adjusting for covariates, GA, birth weight, SGA, female sex, multiple gestation, less invasive resuscitation, and the network of origin showed an independent effect on outcomes. Determining the causes of these differences deserves further study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".