Muscle Size, Strength, and Bone Geometry in the Upper Limbs of Young and Old Men
Bibliographic record
Abstract
BACKGROUND: Bone loss in old men is associated with a decrease in muscle mass and strength. However, the influence of muscle size and strength on age-related changes in bone geometry has not been comprehensively described. Methods. Men in their third (group I, 23 +/- 3 y, n = 20), eighth (group II, 77 +/- 1 y, n = 10), and ninth (group III, 86 +/- 4 y, n = 13) decades of age were studied. The cross-sectional area (CSA) of the elbow flexors, elbow extensors, and forearm muscles, the total area (TA), cortical area (CA), and medullary area (MA) of the midhumerus, and distal third of the radius and ulna (n = 7 group II; n = 6 group III) were measured with magnetic resonance imaging. The maximal isometric strength (MVC) of the elbow flexors and elbow extensors was also determined. RESULTS: The CSA and MVC of the arm muscles (elbow flexors plus elbow extensors) were less in group II (-17% and -22%) and III (-32% and -39%), respectively, compared to group I. However, forearm CSA was less (-21%) in group III only. The TA and MA of all bones were greater in the older groups. The CA of the humerus (-14%) and ulna (-10%), but not the radius, was less in group III compared to group I, whereas CA was unchanged in group II. Stepwise multiple linear regression determined that arm muscle CSA (r = 0.52, p <.01) and forearm muscle CSA (r = 0.41, p <.05) provided the best prediction of CA in the humerus and forearm, respectively. CONCLUSIONS: Muscle size and strength are important determinants of CA in the humerus and forearm. The lower CA in the ninth decade may be explained, in part, by reduced bone strains due to a smaller muscle mass.
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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 | 0.001 |
| 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".