Reproducibility of Air Displacement Plethysmography in Term and Preterm Infants—A Study to Enhance Body Composition Analysis in Clinical Routine
Bibliographic record
Abstract
The quality-initiative analysis of weekly duplicate PEAPOD® body composition measurements was conducted from clinical practice (January to September 2021) on preterm and term infants without respiratory support. Statistical analysis, including regression analysis, Bland–Altman plots and cv-root-mean-square tests, was performed. A total of 188 duplicate (376 individual) measurements were collected from 119 infants (88 preterm, 31 term). The median absolute difference between duplicates was 31.5 g for fat-free mass (FFM). Linear correlation analysis showed R2 = 0.97 for FFM. The absolute differences in FFM and fat mass did not significantly correlate with increasing age. The %FFM differed (p = 0.02) across body weight groups of 1 kg < BW ≤ 2 kg (1.8%; IQR: 0.8, 3.6) and BW > 3 kg (0.9%; IQR: 0.3, 2.1). The median absolute differences were 1 g (IQR: 0.4, 3.1) for body weight and 5.6 mL (IQR: 2.1, 11.8) for body volume. Body volume estimation is charged with a constant absolute error, which is the main factor for differences between repeated body composition assessments. This error becomes more prominent in infants with lower body weights. Nevertheless, reproducibility of weekly PEAPOD testing is sufficient to monitor body compartment changes, offering a foundation for nutritional decisions in both preterm and term infants.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".