Physical activity positively predicts bone architecture and bone strength in adolescent males and females
Bibliographic record
Abstract
AIMS: Physical activity (PA) has positive effects on bone accrual and geometry in children during growth. However, we do not know how PA influences adaptations in bone architecture during growth. We evaluated the contribution of PA to bone density, architecture and strength in adolescents. METHODS: We used HR-pQCT (XtremeCT, Scanco Medical) to assess cross-sectional moments of inertia [Imin, Imax (mm⁴)], total bone density (Tt.Dn, mg HA/cm³), total bone area (Tt.Ar, mm²), cortical bone density (Ct.Dn, mg HA/cm³), cortical thickness (Ct.Th, μm), trabecular bone density (Tb.Dn, mg HA/cm³), trabecular number (Tb.N, mm⁻¹) and trabecular thickness (Tb.Th, μm) at the distal tibia in 146 male and 132 female participants (15-20 years). We evaluated the contribution of impact loading PA (ImpactPA) and non-impact loading PA (NoimpactPA) on bone (p < 0.05). RESULTS: ImpactPA explained 10% of variance in Imin (p = 0.000), and 12% of variance in Imax (p = 0.000) in male participants. In male participants, ImpactPA explained 6% of variance in Tt.Ar (p = 0.003). In female participants, ImpactPA explained 4% of variance in Tt.Dn (p = 0.011), 5% of variance in Tb.Dn (p = 0.004) and 8% of variance in Tb.N (p = 0.001). CONCLUSION: Our findings suggest that ImpactPA is significantly associated with bone architecture and bone strength in adolescent males and females.
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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) | 0.003 | 0.001 |
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".