The Impact of Postnatal Systemic Steroids on the Growth of Preterm Infants: A Multicenter Cohort Study
Bibliographic record
Abstract
Postnatal steroids, often used to prevent and treat bronchopulmonary dysplasia, may influence the growth of preterm infants, although data are scarce in the literature. This is a multicenter cohort study including surviving preterm infants <32 weeks at birth (n = 17,621) from the Spanish Neonatal Network SEN1500 database, without major congenital malformations. Linear regression models were adjusted for postnatal steroids, respiratory severity course (invasive mechanical ventilation at 28 days), progression to moderate–severe bronchopulmonary dysplasia (O2 at 36 weeks), length of stay, sex, gestational age and z-scores at birth. A subgroup analysis depending on the timing of administration, ventilation status at 28 days and moderate–severe BPD diagnosis was also performed. Overall, systemic postnatal steroids were not independently associated with poorer weight gain (0.1; 95% CI: −0.05 to 0.2 g/kg/day), linear growth (0; 95% CI: −0.03 to 0.01 cm/week) or head circumference growth (−0.01; 95% CI: −0.02 to 0 cm/week). Patients who received steroids after 28 days or who were not O2 dependent at 36 weeks after having received steroids gained more weight (0.22; 95% CI: 0.04 to 0.4 and 0.2; 95% CI: 0.004 to 0.5 g/kg/day, respectively). Globally, systemic postnatal steroids had no significant adjusted effect on postnatal growth.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".