Reliability of Air Displacement Plethysmography in a Large, Heterogeneous Sample
Bibliographic record
Abstract
INTRODUCTION: Several studies have assessed the validity of air displacement plethysmography (ADP), but few have assessed the reliability of ADP using a large, heterogeneous sample. PURPOSE: This study was conducted to determine the reliability of ADP using the Bod Pod in a large, heterogeneous sample. METHODS: A total of 980 healthy men and women (30 +/- 15 yr, mean +/- SD) completed two body composition assessments separated by 15-30 min. All testing was done in accordance with the manufacturer's instructions. RESULTS: A significant correlation (r = 0.992, P = 0.001) was found between body density (BD) 1 (1.046 +/- 0.001 kg.L(-1); mean +/- SEM) and BD 2 (1.046 +/- 0.001 kg.L(-1). A paired t-test revealed no significant difference between BD 1 and 2 (P = 0.935). The coefficient of variation (CV) for BD was 0.15%. A significant intraclass correlation coefficient (ICC) was found for BD (ICC = 0.996, P = 0.001), and the standard error of measurement (SEM) was 0.001 kg.L(-1). Body mass (BM) 1 and 2 were correlated significantly (r = 0.999, P = 0.001); however, a significant (P = 0.001) decrease was seen from BM 1 (75.510 +/- 0.461 kg) to BM 2 (75.497 +/- 0.461 kg). Body volume (BV) tended to decrease (P = 0.08) from BV 1 (69.900 +/- 0.449 L) to BV 2 (69.884 +/- 0.449 L). CONCLUSION: ADP using the Bod Pod appears to assess BD reliably; however, the observed CV suggests that multiple trials are necessary to detect small treatment effects.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".