Association between Growth Trajectories and Body Composition Outcomes in Very Preterm Infants: A Cohort Study
Bibliographic record
Abstract
INTRODUCTION: There is a need to establish realistic, rather than idealistic, postnatal growth targets. We aimed to characterize body composition outcomes of preterm infants growing along recently defined individualized growth trajectories. METHODS: In this cohort study, infants born <33 weeks of gestation in the United States, Canada, Germany, and Austria between 2012-2022 were included if they had body composition measurements at term-equivalent age. Growth trajectories for each infant were generated retrospectively based on weight data collected at birth and at term-equivalent age. This allowed for the calculation of the difference between actual and target weight at term-equivalent age or discharge and stratification of infants into three growth trajectories: 1) 100g or further below target, 2) within target (±99g), and 3) 100g or more above target. RESULTS: A total of 1052 infants were included. The median gestational age and birthweight were 28 weeks and 1060g, respectively. A linear correlation between the actual versus target weight difference and fat-free mass (FFM) z-scores was found (r = 0.34, p < 0.0001). Among infants whose weights remained within the target range (30%), the mean FFM z-score was -1.6 [SD: 1.2] and the mean body fat percentage was 15 [SD: 5.9]. In addition to lower mean FFM z-scores, infants whose weight fell below the target range had greater declines in weight, length, and head circumference z-scores. CONCLUSIONS: Weight trajectories below a recently defined target is linked to lower FFM. Further research is needed to determine whether prospectively targeting these individualized growth trajectories improves FFM outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".