Bone strength index in adolescent girls: does physical activity make a difference?
Bibliographic record
Abstract
BACKGROUND: Bone strength index (BSI) combines bone mineral and bone biomechanical properties to measure resistance to bending. This index may have greater clinical significance than the more often described markers of bone mineral content (BMC), areal density, or geometry alone and, in turn, may show a stronger relation to fracture risk. The BSI is the product of volumetric cortical bone mineral density (BMD) and cross sectional moment of inertia within a region of interest. Calculations combine dual energy x ray absorptiometry and magnetic resonance imaging technologies and provide a useful, non-invasive measure of in vivo bone strength. OBJECTIVES: (a) To compare BSI in adolescent female middle distance runners and age matched controls; (b) to examine factors predictive of BSI in adolescent girls. METHODS: Twenty adolescent female middle distance runners (mean (SD) age 16 (1.7) years, physical activity 8.9 (2.1) hours a week) and 20 female controls (age 16 (1.8) years, physical activity 2.0 (0.07) hours a week) were recruited. To calculate BSI, a region of interest representing 10% of the mid-distal tibia was analysed for dual energy x ray absorptiometry derived BMC and was combined with bone geometry and biomechanical properties obtained by magnetic resonance imaging assessments. Potential predictors of BSI were also examined. RESULTS: Independent t tests showed that BMC (p = 0.028), cortical bone volume (p = 0.002), volumetric cortical BMD (p = 0.004), cross sectional moments of inertia (p = 0.005), and BSI (p = 0.002) were higher in the distal tibia of athletes than of controls. The strongest predictor of BSI was hours of physical activity a week (R2 = 0.46). CONCLUSIONS: Athletes habitually exposed to high training loads displayed greater BSI at the distal tibia than controls. The results further confirm BSI as a significant and discerning marker in musculoskeletal health in adolescent girls engaged in high and low mechanical loading.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".