Muscle cross sectional area and grip torque contraction types are similarly related to pQCT derived bone strength indices in the radii of older healthy adults.
Bibliographic record
Abstract
OBJECTIVES: We sought to identify the variance in radius bone strength indices explained by forearm muscle cross sectional area (MCSA) and isometric (ISO), concentric (CON), or eccentric (ECC) grip torque in healthy men and postmenopausal women when gender and body size were controlled for. Additionally we assessed variance in various grip contractions explained by MCSA. METHODS: pQCT estimated bone strength of the radius and forearm MCSA were measured from 45 healthy adults (59.4-/+7.2 yrs). Isokinetic dynamometry was used to assess peak grip torque. Regressions were adjusted for gender and radius length. RESULTS: Peak grip torques were not independent predictors (p>0.05) of distal radius bone strength in compression (bone strength index; BSI(c)) when gender was included in the prediction model. Gender was not a contributor to any model that included MCSA (p>0.05). At the diaphysis all torque measures, MCSA, gender, and length, significantly contributed to predict similar portions (79-80%) of the stress strain index (SSI(p); strength in torsion). MCSA accounted for 68-76% of variance in grip torque (p<0.05). CONCLUSIONS: When estimating radius bone strength, forearm MCSA is a comparable predictor to CON, ISO, and ECC grip torques and is also a good surrogate of these contraction types.
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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 | 0.004 |
| 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) | 0.001 | 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".