Forearm muscle size, strength, force, and power in relation to pQCT-derived bone strength at the radius in adults
Bibliographic record
Abstract
We aimed to examine the relationship between forearm muscle cross-sectional area (MCSA), muscle force, or rate of torque development (RTD) and 2 estimated radius bone strength indices - compressive bone strength index (BSI) at the wrist and strength strain index in torsion (SSI(p)) at the shaft - in healthy middle-aged males and females. Distal (4%) and shaft (65%) sites of nondominant forearms were scanned using peripheral quantitative computed tomography (pQCT) in a sample of 48 adults (mean age ± SD, 49.4 ± 2.4 y) to obtain estimated bone strength indices and MCSA. Muscle force, measured by grip dynamometry and wrist flexion RTD, was obtained using an isokinetic dynamometer. Hierarchical linear regressions, adjusted for weight, explained 27% and 36% of the BSI variance at the 4% site in males and premenopausal females, respectively (p < 0.05). At the radius shaft, weight explained 26% (p < 0.05) and 83% (p < 0.01) of SSI(p) variance. The unique variance of BSI explained by MCSA was 16% in males (p < 0.05) and 31% in females (p < 0.01). Grip force predicted variance in SSI(p) in males (p < 0.01) and BSI in females (p < 0.05). RTD did not explain any variance in BSI or SSI(p). Body weight was the only significant predictor (p < 0.05) of SSI(p) in females. Although forearm muscle size and grip strength are associated with estimates of radius bone strength at midlife, this relationship appears to be sex dependent. The differences observed between muscle size and strength properties and bone strength at distal and shaft sites of the radius suggest a property-, sex-, and site-specific relationship between muscle and bone in the forearm.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".