Effect of physical activity on bone mineral density assessed by limb dominance across the lifespan
Bibliographic record
Abstract
Bone mineral density is higher in dominant vs. nondominant limbs, implying that the greater use of dominant limbs in everyday activities results in the deposition of more bone or that the dominant limb is genetically larger. The objective of the present study was to determine whether bone mineral density differences between dominant and nondominant arms were greater in older vs. younger women. To determine whether this was due to a greater lifetime of preferential loading of the dominant arm, differences between dominant and nondominant arms were compared to accumulated amounts of physical activities which emphasized use of the dominant arm. Bone mineral density of dominant and nondominant arms was assessed by dual-energy X-ray absorptiometry in groups of younger (n = 35; age = 20.9) and older (n = 53; age = 57.4) women. The difference between arms was greater in the older vs. the younger group (5.2% vs. 1.9%, respectively, P < 0.01). Within the older group, total lifetime energy expenditure during activities emphasizing loading of the dominant arm correlated with the bone mineral difference between dominant and nondominant arms (r = 0.47, P < 0.01). This implies that a greater lifetime of preferential loading of the dominant arm in the older group resulted in a greater difference between arms. Am. J. Hum. Biol. 12:633-637, 2000. Copyright 2000 Wiley-Liss, Inc.
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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 | 0.000 | 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".