Accuracy of body composition measurement techniques across the age span
Bibliographic record
Abstract
This study investigated the acceptable accuracy of common body composition techniques compared with the reference 4-compartment (4C-R) model, which has not been investigated in a sample with diverse characteristics, including age and sex. Techniques included components of the 4C-R model [dual-energy X-ray absorptiometry, air displacement plethysmography, deuterium dilution (DD)] and surrogate compartment models, which utilised bioelectrical impedance spectroscopy (BIS) rather than DD. Men and women (sex = 1:1, 18–85 years, n = 90) completed body composition testing under best-practice guidance. For measurement of individuals, only the reference 3-compartment (3C-R) equation met acceptable error limits (<5% error among individuals) within the a priori cut-point (80%) for fat-free mass (FFM; CV = 0.52%) and fat mass (FM; CV = 1.61%). However, all investigated techniques reached equivalency to the 4C-R model for FFM on average (CV = 0.52–4.31%), but for FM only the 3C and 4C equations that included quantification of total body water (TBW) by DD or BIS reached equivalency overall (CV = 1.61–6.68%). Sex and age minimally influenced accuracy. Only the 3C-R or 4C-R equations are supported for acceptable individual accuracy for both FFM and FM. For group estimates any investigated technique could be used with acceptable accuracy for FFM; however, for FM, inclusion of TBW measurement within a compartment model is necessary. Novelty: Only the referent 3C and 4C models (including deuterium dilution) provide accurate body composition results that are acceptable for measurement of individuals in the general population. For group estimates of lean mass in the general population, compartments models that include TBW must be used for accurate measurement.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".