Supplementary Material for: 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
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.605 | 0.091 |
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".