Head, Neck, Trunk, and Pelvis Tissue Mass Predictions for Older Adults using Anthropometric Measures and Dual-Energy X-Ray Absorptiometry
Bibliographic record
Abstract
Background: Regression equations using anthropometric measurements to predict soft (fat mass [FM], lean mass [LM], wobbling mass [WM]) and rigid (bone mineral content [BMC]) tissue masses of the extremities and core body segments have been developed for younger adults (16-35 years), but not older adults (36-65 years). Tissue mass estimates such as these would facilitate biomechanical modeling and analyses of older adults following fall or collision-related impacts that might occur during sport and recreational activities. Purpose: The purpose of this study was to expand on the previously established tissue mass prediction equations of the head, neck, trunk, and pelvis for healthy, younger adults by generating a comparable set of equations for an older adult population. Methods: A generation sample (38 males, 38 females) was used to create head, neck, trunk, and pelvis tissue mass prediction equations via multiple linear stepwise regression. A validation sample (13 males, 12 females) was used to assess equation accuracy; actual tissue masses were acquired from manually segmented full body Dual-Energy X-ray Absorptiometry scans. Results: Adjusted R2 values for the prediction equations ranged from 0.326 to 0.949, where BMC equations showed the lowest explained variances overall. Mean relative errors between actual and predicted masses ranged from –2.6% to 6.1% for trunk LM and FM, respectively. All actual tissue masses except head BMC (R2 = 0.092) were significantly correlated to those predicted from the equations (R2 = 0.403 to 0.963). Conclusion: This research provides a simple and effective method for predicting head, neck, trunk, and pelvis tissue masses in older adults that can be incorporated into biomechanical models for analyzing sport and recreational activities. Future work with this population should aim to improve core segment BMC predictions and develop equations for the extremities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".