Incidence, Treatment, and Outcome Trends of Necrotizing Enterocolitis in Preterm Infants: A Multicenter Cohort Study
Bibliographic record
Abstract
Background: Data regarding the incidence and mortality of necrotizing enterocolitis trends are scarce in the literature. Recently, some preventive strategies have been confirmed (probiotics) or increased (breastfeeding rate). This study aims to describe the trends of necrotizing enterocolitis incidence, treatment and mortality over the last decade in Spain. Methods: Multicenter cohort study with data from the Spanish Neonatal Network - SEN1500 database. The study period comprised from January 2005 to December 2017. Preterm infants < 32 weeks of gestational age at birth without major congenital malformations were included for analysis. The main study outcomes were Necrotizing enterocolitis incidence, co-morbidity (bronchopulmonary dysplasia, late-onset sepsis, cystic periventricular leukomalacia, retinopathy of prematurity, acute kidney injury), mortality and surgical / non-surgical treatment. Results: Among the 25,821 included infants, NEC incidence was 8,8% during the whole study period and remained stable when comparing the 3-year subperiods. However, more cases were surgically treated (from 48.8% in 2005-08 to 70.2% in 2015-17, p<0.001). Mortality improved from 36.7% in the 2005-2008 to 26.6% in 2015-2017 (p<0.001). Breastfeeding rates improved over the studied years (24.3% to 40.5%, p<0.001), while gestational age remained invariable (28.5 weeks, p=0.20). Prophylactic probiotics were implemented during the study period in some units, reaching 18.6% of the patients in 2015-17. Conclusions: The incidence of necrotizing enterocolitis remained stable despite the improvement regarding protective factors frequency. Surgical treatment became more frequent over the study period, whereas mortality decreased.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".