Necrotizing enterocolitis in low birth weight infants in China: Mortality risk factors expressed by birth weight categories
Bibliographic record
Abstract
BACKGROUND: We retrospectively investigated incidence, morbidity, and mortality of neonatal necrotizing enterocolitis in China, with special emphasis on determining the predictors of necrotizing enterocolitis associated mortality. METHODS: We identified neonates as having necrotizing enterocolitis if they met the accepted diagnostic criterion. Data pertaining to antenatal period, labor and birth, and the postnatal course of illness were collected. Multivariate analysis and logistic regression were used to analyze the risk factors. RESULTS: There were 1167 cases of necrotizing enterocolitis identified from the 95 participating NICUs in mainland China in 2011, with the incidence of 2.50% and 4.53% in LBW (birth weight <2500 g) and VLBW (birth weight <1500 g) infants, respectively. Stage 1, 2 and 3 diseases were noted in 51.1%, 30.3% and 18.6% of cases respectively. The mortality from stage 2 and 3 necrotizing enterocolitis in this cohort was 41.7%. In VLBW infants, the important risk factors for mortality were small for gestation age (OR: 5.02, 95% CI 1.73-14.6; P = 0.003) and stage 3 NEC (OR: 8.09, 95% CI 2.80-23.3, P < 0.001). In moderate LBW infants (birth weight 1500-2499 g), the risk factors identified for mortality were sepsis during hospitalization (OR: 2.59, 95% CI 1.57-4.28, P < 0.001) and stage 3 NEC (OR: 5.37, 95% CI 3.24-8.90; P < 0.001). CONCLUSIONS: Necrotizing enterocolitis remains an important cause of morbidity and mortality in prematurely born neonates in Chinese neonatal units. Awareness of the associated risk factors and appropriate interventions may improve the outcome of necrotizing enterocolitis in different birth weight subgroup.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".