Accuracy of aggregate 2- and 3-component models of body composition relative to 4-component for the measurement of changes in fat mass during weight loss in overweight and obese subjects
Bibliographic record
Abstract
The 4-component (4-C) model is the reference method to measure fat mass (FM). Simpler 2-component (2-C) models are widely used to assess FM. We hypothesised that an aggregate 2-C model may improve accuracy of FM assessment during weight loss (WL). One hundred and six overweight and obese men and women were enrolled in different WL programs (fasting, very low energy diet, low energy diet). Body density, bone mineral content, and total body water were measured. FM was calculated using 2-C, 3-C, and 4-C models. Aggregate equations for 2-C, 3-C, and 4-C models were calculated, with the aggregate 4-C model assumed as the reference method. The aggregate approach postulates that the average of the individual estimates obtained from each model is more accurate than the best single measurement. The average WL was -7.5 kg. The agreement between 3-C and 4-C models for FM change was excellent (R(2) = 0.99). The aggregate 2-C equation was more accurate than individual 2-C estimates in measuring changes in FM. The aggregate model was characterised by a lower measurement error at baseline and post-WL. The relationship between the aggregate 3-C and 4-C component models was highly linear (R(2) = 0.99), whereas a lower linearity was found for the aggregate 2-C and 4-C model (R(2) = 0.72). The aggregate 2-C model is characterised by a greater accuracy than commonly applied 2-C equations for the measurement of FM during WL in overweight and obese men and women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".