Variations in length of stay among survived very preterm infants admitted to Chinese neonatal intensive care units
Bibliographic record
Abstract
BACKGROUND: This study aimed to describe length of stay (LOS) to discharge and site variations among very preterm infants (VPIs) admitted to 57 Chinese neonatal intensive care units (NICUs) and to investigate factors associated with LOS for VPIs. METHODS: This retrospective multicenter cohort study enrolled all infants < 32 weeks' gestation and admitted to 57 NICUs which had participated in the Chinese Neonatal Network, within 7 days after birth in 2019. Exclusion criteria included major congenital anomalies, NICU deaths, discharge against medical advice, transfer to non-participating hospitals, and missing discharge date. Two multivariable linear models were used to estimate the association of infant characteristics and LOS. RESULTS: A total of 6580 infants were included in our study. The overall median LOS was 46 days [interquartile range (IQR): 35-60], and the median corrected gestational age at discharge was 36 weeks (IQR: 35-38). LOS and corrected gestational age at discharge increased with decreasing gestational age. The median corrected gestational age at discharge for infants at 24 weeks, 25 weeks, 26 weeks, 27-28 weeks, and 29-31 weeks were 41 weeks, 39 weeks, 38 weeks, 37 weeks and 36 weeks, respectively. Significant site variation of LOS was identified with observed median LOS from 33 to 71 days in different hospitals. CONCLUSIONS: The study provided concurrent estimates of LOS for VPIs which survived in Chinese NICUs that could be used as references for medical staff and parents. Large variation of LOS independent of infant characteristics existed, indicating variation of care practices requiring further investigation and quality improvement.
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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 | 0.000 | 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".