Association of Neonatal Morbidities and Postnatal Growth Faltering in Preterm Neonates
Bibliographic record
Abstract
Background/Objectives: Postnatal growth faltering (PGF) is a risk factor for adverse neurodevelopment in very preterm neonates. The aim of this retrospective study was to determine which infants’ baseline characteristics, prenatal risk factors and neonatal morbidities are associated with two definitions of PGF: defined as loss of >2 weight z-scores (severe PGF) or as loss of >1 weight, length, and head circumference z-scores between birth and discharge (complex PGF); Methods: 146 premature newborns (<32 weeks of gestational age, <1500 g) were included in the study. Anonymized data including anthropometric measurements (weight, length, and head circumference), perinatal and neonatal data (demographics, maternal morbidities and previous pregnancies, and neonatal and perinatal morbidities) were extracted from the clinical electronic database. Changes in anthropometric age- and sex-specific z-scores using the Fenton 2013 preterm growth charts were calculated to diagnose severe PGF and complex PGF; Results: The incidence of severe PGF was 11% and complex PGF was 24%. Both PGF definitions were associated with bronchopulmonary dysplasia (BPD), severe retinopathy of prematurity (ROP), longer respiratory support, and longer hospital stay. Severe PGF was associated with surgical necrotizing enterocolitis at 25% vs. 1.5%, p = 0.001. Complex PGF was associated with severe brain injury at 51% versus 27%, p = 0.007. Complex PGF was more common in newborns born most prematurely, while severe PGF was more common in newborns born small for gestational age (SGA); Conclusions: Both severe and complex PGF are associated with several important neonatal morbidities, which might explain why growth faltering is associated with suboptimal neurodevelopment. Appropriate early identification of faltered growth may influence medical and nutrition interventions which in turn could improve the outcome of very preterm newborns.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".