Improved Outcomes of Very Low Birth Weight Infants in Korea: 2015–2016 vs. 2021–2022 From the Korean Neonatal Network
Bibliographic record
Abstract
Background: This study aimed to analyze improvements in the survival rates and morbidities of very low birth weight infants (VLBWIs) in the Republic of Korea from 2015 to 2022.Methods: Data from the Korean Neonatal Network were used to assess survival and morbidity trends among VLBWIs, particularly focusing on changes across different gestational ages.The analysis included a comparison with data from other neonatal networks, such as the Australian and New Zealand Neonatal Network, Canadian Neonatal Network, Swedish Neonatal Quality Register, Neonatal Research Network Database Japan, and United States National Vital Statistics Reports, to understand differences in survival and morbidity outcomes.Results: The survival rate of VLBWIs in Korea rose from 86% in 2015-2016 to 91% in 2021-2022.This increase was most notable in infants born before 28 gestational weeks, with significant gains observed among those born at 24 (67% in 2021-2022 compared with 51% in 2015-2016, P < 0.001), 25 (80% in 2021-2022 versus 66% in 2015-2016, P < 0.001), and 26 (89% in 2021-2022 compared to 79% in 2015-2016, P < 0.001) gestational weeks.Compared with other networks, the survival rates of Korean VLBWIs, except for infants born before 24 gestational weeks whose survival rates remain significantly lower, have largely caught up with those of high-income countries.The incidence of major morbidities such as severe intraventricular hemorrhage, necrotizing enterocolitis, and sepsis also showed a decreasing trend.However, incidence rates of bronchopulmonary dysplasia and periventricular leukomalacia remained relatively high, especially for infants born before 24 gestational weeks.Conclusion: Although outcomes have improved significantly in Korea, further efforts are needed to enhance survival rates of infants born before 24 gestational weeks.Focused interventions and improvements in clinical practices are essential to achieve better outcomes for these vulnerable infants.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 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".