Standardizing Neonatal Body Composition Assessment Using Air Displacement Plethysmography: Insights from the Bavarian Experience
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: system, is well established in research and increasingly explored in clinical practice. Building on our team's earlier experiences, this study aimed to (1) evaluate the safety and feasibility of ADP in preterm infants, (2) identify published clinical protocols, and (3) implement and assess a standardized routine-the Bavarian Clinical Protocol (BCP). METHODS: We conducted two systematic literature reviews: one on the eligibility-to-assessment rate and safety of ADP in research contexts, and a second focusing on existing clinical protocols. In addition, we retrospectively analyzed routine ADP assessments at the NICU of Nuremberg Children's University Hospital from January 2022 to December 2024, where the BCP had been introduced. RESULTS: The literature review included 76 studies reporting a total of 8,317 assessments without adverse events. In experimental settings, the eligibility-to-assessment rate was 41%. We identified three published clinical protocols. Following BCP implementation, 626 of 702 eligible infants (89.1%) underwent a total of 851 ADP measurements. No adverse events were observed, and repeated assessments were integrated smoothly into clinical workflows. CONCLUSIONS: ADP can be safely and effectively incorporated into neonatal routine care. The Bavarian Clinical Protocol provides a practical framework for standardized application, improves comparability across centers, and supports the clinical use of body composition data to inform individualized nutritional strategies.
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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 | 0.092 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".