PO-0459 Fat Mass(fm) And Fat Free Mass(ffm) Indices In Preterm And Term Infants During First 6 Months Of Life
Bibliographic record
Abstract
Background The development of normative reference Body Composition (BC) data in infancy, is an important step towards evaluation of postnatal growth in clinical practice. Weight gain fails to differentiate lean body mass (constitution) from fat mass (nutrition). Percentile ranks and length normalised indices of FFM and FM can define nutritional depletion or obesity. We present longitudinal data from a large dataset of stable growing infants. Methods BC data (n = 857) from 574 infants (22–42 wks) enrolled in 4 longitudinal studies, 2/3rd were preterm, was considered as independent data points. Preterms on fortified breast milk or formula (80 kcal/dL), term infants on breast milk or formula (67 kcal/dL). Time points of measurement : after reaching full enteral feeding, at term and two further time points until a maximum of 6 months of corrected age. BC was measured by Dual energy X-ray absorptiometry (QDR 1500; Hologic). R software (GAMLSS) used for construction of growth curves. Results Length of preterms remain shorter than terms, both increases linearly at 0.7 cm/wk. Median FM/FFM in preterm is 500 g/2450 g (40 wks) and 1700 g/4500 g (60 wks) compared to 430 g/2790 g (40 wks) and 2400 g/4700 g (60 wks) for term. Preterm FMI centiles are higher than terms till 45–50 wks corrected. Preterm FFMI increases progressively till 40 wks, then remains constant over time like terms (Figure 1). Conclusion Growth pattern for preterm vary from term, justifying their higher nutritional requirement to support rapid FFM growth initially. FM being inverse of FFM, length normalised indices rather than percentages allow independent assessment of growth in each body compartment, while compensating for difference in body size of term and preterm infants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 0.001 |
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".