Variations in Incidence of Necrotizing Enterocolitis in Canadian Neonatal Intensive Care Units
Bibliographic record
Abstract
OBJECTIVES: Necrotizing enterocolitis (NEC) is the most common acquired intestinal disease of neonates. Previous reports on incidence have generally examined small cohorts of extremely low-birth-weight infants and have not examined risk-adjusted variations among neonatal intensive care units (NICUs). The authors examined risk-adjusted variations in the incidence of NEC in a large group of Canadian NICUs and explored possible therapy-related risks. METHODS: The authors obtained data on 18,234 infants admitted to 17 tertiary level Canadian NICUs from January 1996 to October 1997. They used multivariate logistic regression analysis to examine the inter-NICU variation in incidence of NEC, with adjustment for population risk factors and admission illness severity, and explored therapy-related variables. RESULTS: The incidence of NEC was 6.6% (n = 238) among 3,628 infants with birth weight < or = 1,500 g (VLBW), and 0.7% (n = 98) among 14,606 infants with birth weight > 1,500 g (HBW). Multivariate logistic regression analysis showed that for VLBW infants, NEC was associated with lower gestational age and treatment for hypotension and patent ductus arteriosus. Among HBW infants, NEC was associated with lower gestational age, presence of congenital anomalies (cardiovascular, digestive, musculoskeletal, multiple systems) and need for assisted ventilation. There was no significant variation in the risk-adjusted incidence of NEC among NICUs, with the exception of one NICU reporting no cases of NEC. CONCLUSIONS: Risk factors for NEC were different in VLBW and HBW infants. There was no significant variation in the risk-adjusted incidence of NEC among Canadian NICUs, with one possible exception.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".