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 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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".