A Selected Review of the Mortality Rates of Neonatal Intensive Care Units
Bibliographic record
Abstract
INTRODUCTION: Newborn babies in need of critical medical attention are normally admitted to the neonatal intensive care unit (NICU). These infants tend to be preterm, have low birth weight, and/or have serious medical conditions. Neonatal survival varies, but progress in perinatal and neonatal care has notably diminished mortality rates. In this selected review, we examine and compare the NICU mortality rates and etiologies of death in different countries. METHODS: A literature search was conducted in Ovid MEDLINE, OLDMEDLINE, EMBASE Classic, and EMBASE. The primary endpoint was the mortality rates in NICUs. Secondary endpoints included the reasons for death and the correlation between infant age and mortality outcome. For the main analysis, we examined all infants admitted to NICUs. Subgroup analyses included extremely low birth weight infants (based on the authors' own definition), very low birth weight infants, very preterm infants, preterm infants, preterm infants with a birth weight of ≤1,500 g, and by developed and developing countries. RESULTS: The literature search yielded 1,865 articles, of which 20 were included. The total mortality rates greatly varied among countries. Infants in developed and developing countries had similar ages at death, ranging from 4 to 20 days and 1 to 28.9 days, respectively. The mortality rates ranged from 4 to 46% in developed countries and 0.2 to 64.4% in developing countries. CONCLUSION: The mortality rates of NICUs vary between nations but remain high in both developing and developed countries.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".