Factors Influencing Body Composition of Premature Infants at Term‐Adjusted Age
Bibliographic record
Abstract
We investigated the influence of body size at birth, feeding of mother's milk versus formula, or standard-term formula versus energy- or nutrient-enriched formula on the growth and whole body bone mineral content (BMC), lean and fat mass (using dual-energy X-ray absorptiometry) of low-birth-weight (LBW) infants to term-adjusted age. LBW infants who were appropriate for gestational age were lighter and shorter than term-born infants (n = 46) but had a higher percent fat mass (19-28% vs. 15 +/- 7%). For LBW infants fed standard formula or mother's milk after hospital discharge, the mean BMC expressed either as a function of weight (17 +/- 2, 19 +/- 2 vs. 20 +/- 2 g/kg) or length (1.1 +/- 0.2, 1.1 +/- 0.2 vs. 1.5 +/- 0.2 g/cm) was more than 1 SD below term infant values. However, infants fed a nutrient-enriched formula from hospital discharge had BMC within 1 SD below term infants. Infants who were born small, compared to appropriate for gestational age, compared to infants of similar birth weight had lower percent body fat (16 +/- 6 vs. 19 +/- 5) and lower BMC (47 +/- 3 vs. 62 +/- 5 g) at term age. Both size at birth and diet influence patterns of growth and body composition in early life in very-low-birth-weight (VLBW) infants. The long-term significance of these variable growth patterns in VLBW infants in early life requires further investigation.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".