Infant body composition in the PEA POD<sup>®</sup> era: what have we learned and where do we go from here?
Bibliographic record
Abstract
The availability of clinically feasible infant body composition assessment can inform current questions regarding the developmental origins of chronic disease. A strategic approach will facilitate more rapid advancement in knowledge. The objective of this study was to summarize published evidence and ongoing research activity in infant body composition using the PEA POD® infant body composition system. All published studies using the PEA POD® were identified and grouped according to study population and question. All centers with PEA POD® units were invited to participate in an online survey regarding past, current and future PEA POD® use, and results were analyzed using descriptive statistics. The resulting information was used to identify gaps or limitations in existing knowledge, thus highlighting potential research priorities. Twenty-seven published articles were identified and grouped into six research themes. Although the number of infants studied is significant in some areas, interpretation of data is limited by methodological differences. Survey responses were received from 16 of ∼60 centers. Research themes echoed those identified from the published literature. Controlling for or reporting potential confounding variables is essential for understanding infant body composition data. Measurement of health outcome variables would be helpful in identifying associations.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".