Head, Neck, Trunk, and Pelvis Tissue Mass Predictions for Young Adults Using Anthropometric Measures and Dual-Energy X-Ray Absorptiometry
Bibliographic record
Abstract
Accurate prediction of wobbling mass (WM), fat mass (FM), lean mass (LM), and bone mineral content (BMC) of living people using regression equations developed from anthropometric measures (lengths, circumferences, breadths, skinfolds) has previously been reported, but only for the extremities. Multiple linear stepwise regression was used to generate comparable equations for the head, neck, trunk, and pelvis of young adults (38 males, 38 females). Equations were validated using actual tissue masses from an independent sample of 13 males and 13 females by manually segmenting full-body dual-energy x-ray absorptiometry scans. Prediction equations exhibited adjusted R 2 values ranging from .249 to .940, with more explained variance for LM and WM than BMC and FM, especially for the head and neck. Mean relative errors between predicted and actual tissue masses ranged from −11.07% (trunk FM) to 7.61% (neck FM). Actual and predicted tissue masses from all equations were significantly correlated ( R 2 = .329 to .937), except head BMC ( R 2 = .046). These results show promise for obtaining in-vivo head, neck, trunk, and pelvis tissue mass estimates in young adults. Further research is needed to improve head and neck FM and BMC predictions and develop tissue mass prediction equations for older populations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".