Improvement in the Survival Rates of Very Low Birth Weight Infants after the Establishment of the Korean Neonatal Network: Comparison between the 2000s and 2010s
Bibliographic record
Abstract
The survival rate (SR) of very low birth weight infants (VLBWIs) and extremely low birth weight infants (ELBWIs) is a health indicator of neonatal intensive care unit (NICU) outcomes. The Korean Neonatal Network (KNN) was established in 2013, and a system has been launched to manage the registration and quality improvement of VLBWIs. The SR of the VLBWIs significantly increased to 85.7% in the 2010s compared with 83.0% in the 2000s. There was also a significant increase in the SR of the ELBWIs from 66.1% to 70.7%. The equipment, manpower, and assistance systems of NICUs also improved in quantity and quality. In the international comparison of the SRs of VLBWIs, the SRs were 93.8%, 92.2%, 90.2%, 89.4%, 86.4%, 85.1%, and 80.6% in Japan, Australia and New Zealand, Canada, Europe, Korea, Taiwan, and United States, respectively. In conclusion, the SRs of the VLBWIs and ELBWIs improved in the 2010s compared with those in the 2000s in Korea. This improvement is considered to have been related to the role of the KNN built in 2013. However, the latest VLBWI and ELBWI SRs in 2015 are still low compared with those in Japan, Australia and New Zealand, Canada, and Europe. In the future, we must establish and develop the tasks that are presented as future tasks in this review.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".