Bibliographic record
Abstract
In Korea, the first neonatal intensive care unit was established in the early 1980s, and over the past 30 years, the advancements in the field of neonatology in Korea have led to a significant improvement in the survival of low birth weight infants. The survival rates of very low birth weight infants (VLBWIs) more than doubled, from 38.3% in the 1980s to 84.8% in 2014, and the survival rates of extremely low birth weight infants increased more than five-fold, from 12.3% in the 1980s to 69.6% currently. A comparison of VLBWI survival among countries showed improved survival rates in each birth weight group in Canada, Australia-New Zealand, and various European countries, with Japan at the top. For the first time in Korea, a nationwide prospective web-based registration system for VLBWIs, the Korean Neonatal Network (KNN), was established, and KNN operations were initiated officially on April 15, 2013 by the Korean Society of Neonatology with support from the Korea Centers for Disease Control and Prevention. As of April 2016, clinical data for over 6,700 VLBWIs have been collected from 64 participating hospitals across the country. This network has made it possible to investigate overall survival rates as well as short- and long-term outcomes in VLBWIs. The purpose of this review was to evaluate the recent changes in neonatal outcomes in VLBWIs in Korea based on KNN data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".