The Relationship of Poor Linear Growth Velocity with Neonatal Illness and Two-Year Neurodevelopment in Preterm Infants
Bibliographic record
Abstract
BACKGROUND: Poor postnatal weight gain in very low birth weight (VLBW) preterm infants has been shown to have a negative effect on neurodevelopment. However, the dose-dependent neurodevelopmental consequences of linear stunting in this population have not previously been assessed. Understanding this relationship is important because organ growth and differentiation are more tightly linked to lean body mass and thus linear growth. OBJECTIVE: To assess the duration and clinical determinants of poor linear growth and its relationship to neurodevelopment in preterm infants. METHODS: Weight, recumbent length and head circumference were recorded at birth, hospital discharge, and at 4, 12 and 24 months corrected age (CA) in 62 VLBW infants. Standardized Z-scores for weight (WZ), length (LZ) and head circumference (HCZ) were calculated and assessed as a function of inpatient clinical factors using linear regression models. Twenty-four-month neurodevelopmental function was analyzed as a function of growth status. RESULTS: Mean LZ was lower than WZ (p = 0.004) at hospital discharge, was related in part to illness severity and remained lower than baseline LZ until 24 months CA. Controlling for WZ and HCZ at each age, lower LZ at 4 and 12 months CA was associated with lower cognitive function scores at 24 months CA (p ≤ 0.03). CONCLUSIONS: Nutritional and non-nutritional factors influenced the degree of pre- and postdischarge linear growth suppression in VLBW infants, which in turn was negatively associated with developmental outcomes at 24 months CA. Since linear growth correlates with brain growth and indexes a number of clinical factors, it is an important biomarker that can be used in VLBW infants to predict long-term developmental outcomes.
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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 | 0.005 |
| 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 | 0.000 |
| 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".