Pattern of growth of very low birth weight preterm infants, assessed using the WHO Growth Standards, is associated with neurodevelopment
Bibliographic record
Abstract
Several Canadian professional organizations recently recommended that the growth of preterm infants be monitored using the World Health Organization Growth Standards (WHO-GS) after hospital discharge. The WHO-GS are a prescriptive set of growth charts that describe how term infants should grow under ideal environmental conditions. Whether preterm infants following this pattern of growth have better outcomes than infants that do not has yet to be evaluated. Our aim was to determine whether the pattern of growth of very low birth weight (VLBW) infants during the first 2 years, assessed using the WHO-GS or the traditional Centers for Disease Control and Prevention reference growth charts (CDC-RGC), is associated with neurodevelopment. Pattern of weight, length, and head circumference gain of appropriate-for-gestation VLBW preterm infants (n = 289) from birth to 18-24 months corrected age was classified, using the WHO-GS and CDC-RGC, as sustained (change in Z-score ≤1 SD), decelerated (decline >1 SD), or accelerated (incline >1 SD). Development was assessed using the Bayley Scales of Infant and Toddler Development (BSID)-III at 18-24 months corrected age. Using the WHO-GS, children with a decelerated pattern of weight gain had lower cognitive (10 points), language (6 points), and motor (4 points) scores than infants with sustained weight gain (p < 0.05), even after adjustment for morbidities. No association was found using the CDC-RGC. In conclusion, a decelerated pattern of weight gain, determined with the WHO-GS, but not the CDC-GRC, is associated with poorer neurodevelopment scores on the BSID-III than a pattern of sustained 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 | 0.001 | 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 | 0.000 |
| 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".