Total and femoral neck bone mineral density and physical activity in a sample of men and women
Bibliographic record
Abstract
Physical activity (PA), total body fat (TBF), and lean body mass (LBM) are associated with bone mineral density (BMD). However, the independent influence of PA on BMD, while controlling for body composition is not understood as well and is the purpose of the current study. Whole-body BMD (g·cm⁻²), femoral neck BMD (g·cm⁻²), TBF (kg), and LBM (kg) were measured with dual-energy X-ray absorptiometry. PA levels (total, work, sport, non-sport) were estimated using the Baecke questionnaire. General linear models determined the independent effects of PA on BMD (whole-body and femoral neck), with adjustment for age, sex, ethnicity, smoking, menopausal status (as appropriate), LBM, and TBF. These associations were also examined by sex and age group (20-34, 35-49, and 50-64 years). The sample included 802 adults (65% women; 13% African American) from the Pennington Center Longitudinal Study that were 20 to 64 years of age (mean ± SD: 46.9 ± 11.0 years). Higher sports scores were associated with higher femoral neck BMD in the total group, men and women, and in 20- to 34-year-olds and 35- to 49-year-olds, but not significant in those 50-64 years of age. Similar significant associations were found for sports score with total body BMD; however, this relationship was not significant for women or for those 50-64 years of age. Total PA had inconsistent relationships with both femoral neck BMD and total body BMD. Higher levels of sport-related PA are associated with higher femoral neck BMD; however, these relationships vary by PA domain and site of BMD measurement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".