Mortality of neonatal respiratory failure from Chinese northwest NICU network
Bibliographic record
Abstract
Objectives: We aimed to evaluate the efficacy of respiratory support and surfactant in incidence, management and outcome of neonatal hypoxemic respiratory failure (NRF) in Chinese emerging regional neonatal–perinatal care system in the era of universal health insurance policy. Study design: Clinical data of NRF were prospectively collected in 12 consecutive months from 2011 to 2012 in 12 neonatal intensive care units (NICU) in major cities of Northwest China. NRF was defined as hypoxemia requiring nasal continuous positive airway pressure (nCPAP) or intratracheal ventilation combined with surfactant for at least 24 h, with associated risk factors, mortality rate and major co-morbidities analyzed. Results: Among 9816 admissions, there were 1324 NRF cases with 60.2% being preterm. The incidence of NRF was 13.4% with a mortality of 15.5%. The major underlying diseases were respiratory distress syndrome (RDS, 38.9%) and pneumonia/sepsis (38.0%). Only 15.9% of NRF and 33.8% of RDS received surfactant, which contributed to >70% and >85% survival in RDS patients of birth weight (BW) < 1500 g and >1500 g, respectively. Multivariate logistic regression analysis showed that premature rupture of membrane ≥ 24 h, very low BW and gestational age < 32 weeks, resuscitation at delivery, illness severity at admission, intratracheal ventilation and sepsis were the independent risk factors for the mortality of NRF. The length and cost of NICU stay for survivors reflected care burden in the era of universal health insurance. Conclusions: Surfactant significantly improved the survival of neonates with NRF and RDS, reflecting the respiratory care standard in emerging regional neonatal–perinatal care network with limited resources.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".