Individualized Postnatal Growth Trajectories for Preterm Infants
Bibliographic record
Abstract
BACKGROUND: Growth of preterm infants is monitored using fetal charts despite individual trajectories being downshifted postnatally by adaptational processes. The study aims to compare different approaches to create individualized postnatal trajectories. METHODS: Three approaches to achieve growth similar to healthy term infants at 42+0/7 weeks postmenstrual age (PMA) on World Health Organization growth standards (WHOGS) (target weight) were tested by comparing trajectories obtained by: 1) following birth percentiles (Birth-Weight-Percentile Approach); 2) following percentiles achieved at day of life 21 (Postnatal-Percentile Approach); 3) using day-specific fetal median growth velocities starting at day of life 21 (Fetal-Median-Growth Approach [FMGA]). The primary outcome was delta weight (ΔW), defined as difference between target weight (WHOGS) at 42+0/7 weeks and weight predicted by trajectories. The secondary outcome was ΔW vs %fat mass in a cohort of 20 disease-free surviving very low-birth-weight infants. RESULTS: Birth-Weight-Percentile and Postnatal-Percentile Approach showed high ΔW; FMGA alone reduced ΔW. Introducing a factor to FMGA to reflect the transition to extrauterine conditions (Growth-Velocity Approach [GVA]) minimized ΔW. GVA merged with target and best normalized for body composition related to ΔW. CONCLUSIONS: GVA provides an evidence-based approach for individualized growth trajectories. GVA is based on physiologic data and that healthy preterm infants adjust their postnatal trajectory below their birth percentile. GVA may reflect a biologic principle because it matches consistently with WHOGS at 42+0/7 weeks for all preterm infants from 24 to 34 weeks. This concept could become a bedside tool to aid clinicians in monitoring growth, guiding nutrition, and minimizing chronic adult disease risks as a consequence of unguided, inappropriate growth.
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How this classification was reachedexpand
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".