Site-Specific Variance in Radius and Tibia Bone Strength as Determined by Muscle Size and Body Mass
Bibliographic record
Abstract
PURPOSE: To investigate the predictive ability of muscle cross-sectional area (MCSA) and body mass on bone mineral content, compressive bone strength index (BSIc), and the polar stress-strain index (SSIp) of the forearms and lower legs of middle-aged adults. METHODS: A total of 53 healthy adults (37 male, 16 female; mean age 50.4; SD 2.1 y) were scanned with peripheral quantitative computed tomography (pQCT) to measure radius and tibia total and cortical bone mineral content, BSIc, SSIp, and forearm and lower-leg MCSA (BSIc: 4% distal; SSIp and MCSA at 65% and 66% radius and tibia shaft sites, respectively). Multiple regression models adjusted for sex and height were used to assess the relative variance in radius or tibia bone outcomes predicted by body mass and/or forearm or lower-leg MCSA. RESULTS: Forearm MCSA independently predicted total bone-mineral content, BSIc, and SSIp in radius (r partial=0.59, 0.56, 0.42). Body mass was a negative predictor of radius BSIc (r partial=-0.32) and did not predict other radius outcomes when both body mass and MCSA were forced in the models. In the lower leg shaft, MCSA, and body mass predicted bone content and strength similarly when independently added to the models with sex and height. CONCLUSIONS: Forearm MCSA was a dominant predictor of radius bone content and estimated strength. In the tibia, both body mass and lower-leg MCSA contributed to predicting bone content and estimated strength.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".