Body Composition Analysis of the Clinical Routine Using Air Displacement Plethysmography: Age-Group-Specific Feasibility Analysis among Preterm Infants
Bibliographic record
Abstract
Body composition assessments using air displacement plethysmography (ADP, PEAPOD®) have been introduced into clinical practice at a few neonatal units. To allow accurate body composition assessments in term and preterm infants, a workflow for routine testing is needed. The aim of this study was to analyze the feasibility of weekly routine ADP testing. We analyzed (1) postnatal ages at first ADP assessment, (2) the number of weekly routine in-hospital assessments, and (3) the workload of body composition measurements using ADP in clinical practice on the basis of an retrospective analysis of our own clinical operating procedures. The retrospective analysis of weekly routine ADP testing proved feasible at Nuremberg Children’s Hospital. The analysis of postnatal age at the first ADP test revealed differences across groups, with extremely preterm infants starting at a mean postmenstrual age of 36.6 weeks, very preterm infants starting at 34.2 weeks, and moderate to late preterm infants starting at 35.3 weeks. The mean number of tests before discharge was significantly greater in the extremely preterm group (n = 3.0) than in the very preterm (n = 2.4) and moderate to late preterm groups (n = 1.7). The workload of the procedure is reasonable, at 8–13 min per test cycle. The study proved that weekly routine ADP assessments in preterm infants are feasible. However, the initiation of routine testing in extremely preterm infants starts at a significantly greater postnatal age than in the more mature population. ADP assessments can be safely and easily integrated into clinical practice and may be valuable tools for providing additional information on nutritional status and infant growth. A standardized routine protocol allowing identical measurement conditions across healthcare institutions and a standardized interpretation tool for age-adapted body composition data, however, would improve comparability and usability.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".