Length Normalized Indices for Fat Mass and Fat-Free Mass in Preterm and Term Infants during the First Six Months of Life
Bibliographic record
Abstract
OBJECTIVE: Postnatal tissue accretion in preterm infants differs from those in utero, affecting body composition (BC) and lifelong morbidity. Length normalized BC data allows infants with different body lengths to be compared and followed longitudinally. This study aims to analyze BC of preterm and term infants during the first six months of life. METHODS: The BC data, measured using dual energy X-ray absorptiometry, of 389 preterm and 132 term infants from four longitudinal studies were combined. Fat-mass/length² (FMI) and fat-free mass/length² (FFMI) for postmenstrual age were calculated after reaching full enteral feeding, at term and two further time points up to six months corrected age. RESULTS: Median FMI (preterm) increased from 0.4 kg/m² at 30 weeks to 2.5, 4.3, and 4.8 kg/m² compared to 1.7, 4.7, and 6 kg/m² in term infants at 40, 52, and 64 weeks, respectively. Median FFMI (preterm) increased from 8.5 kg/m² (30 weeks) to 11.4 kg/m² (45 weeks) and remained constant thereafter, whereas term FFMI remained constant at 11 kg/m² throughout the tested time points. CONCLUSION: The study provides a large dataset of length normalized BC indices. Followed longitudinally, term and preterm infants differ considerably during early infancy in the pattern of change in FMI and FFMI for age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".