Use of antenatal corticosteroids among infants with gestational age at 24 to 31 weeks in 57 neonatal intensive care units of China: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Antenatal corticosteroids (ACS) can significantly improve the outcomes of preterm infants. This study aimed to describe the ACS use rates among preterm infants admitted to Chinese neonatal intensive care units (NICU) and to explore perinatal factors associated with ACS use, using the largest contemporary cohort of very preterm infants in China. METHODS: This cross-sectional study enrolled all infants born at 24 +0 to 31 +6 weeks and admitted to 57 NICUs of the Chinese Neonatal Network from January 1st, 2019 to December 30th, 2019. The ACS administration was defined as at least one dose of dexamethasone and betamethasone given before delivery. Multiple logistic regressions were applied to determine the association between perinatal factors and ACS usage. RESULTS: A total of 7828 infants were enrolled, among which 6103 (78.0%) infants received ACS. ACS use rates increased with increasing gestational age (GA), from 177/259 (68.3%) at 24 to 25 weeks' gestation to 3120/3960 (78.8%) at 30 to 31 weeks' gestation. Among infants exposed to ACS, 2999 of 6103 (49.1%) infants received a single complete course, and 33.4% (2039/6103) infants received a partial course. ACS use rates varied from 30.2% to 100% among different hospitals. Multivariate regression showed that increasing GA, born in hospital (inborn), increasing maternal age, maternal hypertension and premature rupture of membranes were associated with higher likelihood to receive ACS. CONCLUSIONS: The use rate of ACS remained low for infants at 24 to 31 weeks' gestation admitted to Chinese NICUs, with fewer infants receiving a complete course. The use rates varied significantly among different hospitals. Efforts are urgently needed to propose improvement measures and thus improve the usage of ACS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".