Neonatal Outcomes of Extremely Preterm Infants from Taiwan: Comparison with Canada, Japan, and the USA
Bibliographic record
Abstract
BACKGROUND: This study compared the current trend in survival rates and morbidity for very low birth weight (VLBW) infants in five Medical Training Centers of Prematurity for the Premature Baby Foundation of Taiwan (PBFT), with the outcomes from the USA, National Institute of Child Health and Human Development Neonatal Research Network (NICHD NRN), the Canadian Neonatal Network (CNN), and the Neonatal Research Network of Japan (NRNJ). METHODS: The survival rates of VLBW infants according to gestational age (GA) and major morbidities were compared between networks (Taiwan, USA, Canada, and Japan). Taiwanese data for VLBW infants of GA ≤28 weeks between 2007 and 2012 were obtained from the "PBFT Annual Conferences of Premature Care" reports defining survival rate as neonates that survived to the time of discharge. Major morbidities included severe neurological injury (Grade 3 or 4 intraventricular hemorrhage or periventricular leukomalacia), bronchopulmonary dysplasia, severe retinopathy of prematurity, necrotizing enterocolitis, late-onset sepsis, and patent ductus arteriosus. RESULTS: The survival rates of VLBW infants of GA ≤28 weeks from the PBFT (Taiwan), NICHD NRN (USA), CNN (Canada), and NRNJ (Japan) were 77% (1323/1718), 72% (6859/9575), 82% (2353/2872), and 89% (4489/5069), respectively. The annual survival rates in Taiwan from 2007 to 2012 were 72%, 76%, 76%, 74%, 77%, and 78%, respectively. When GA from ≤23 weeks to 28 weeks was assessed in Taiwan, the survival rates of VLBW infants according to each week were 22%, 50%, 70%, 80%, 88%, and 92%, respectively. The survival rate, especially at lower GAs, was highest in the NRNJ (Japan). The major difference between Taiwan and Japan was attributed to the lower survival rates at lower GA (≤26 weeks) in Taiwan. Japan had the lowest rates of major morbidities among the four countries. CONCLUSION: The survival rate of VLBW infants has improved over the past 6 years in Taiwan. It is higher than the USA, but lower than Canada and Japan. However, the results from Taiwan are from five Medical Training Centers for the PBFT rather than from a population-based study. It is crucial to have a nationwide neonatal research network to develop new practical approaches for VLBW infants in Taiwan.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".