Re-defining normal: bone mineral density in elite female athletes
Bibliographic record
Abstract
Background College age female athletes are susceptible to decreased bone mineral density (BMD) which puts them at an elevated risk for poor bone health in the future. Objective The primary purpose of this study was to compare site-specific and total body BMD of college-age female athletes across sports of varying impact level. Secondary objectives included (1) evaluating the relationship between BMD and menstrual, family bone health, and personal bone health histories; (2) to determine the best clinical predictors of BMD in this population. Design Cross-sectional. Setting NCAA Division I female athletes screened at university sports medicine centre. Participants 265 (20.1±1.19 years, 66.5±10.9 kg, 1.7±0.09 m) college-age female athletes from 14 different sporting teams. Interventions Personal and family health questionnaire, Total Body (TB), anteroposterior Spine (APS), Dual Femur (DF) BMD and Body Composition scans using GE Lunar iDXA. Main outcome measurements Athletes were separated into three sport impact categories. BMD measurements were compared across the impact levels. Both stepwise regression and classification and regression tree analysis were conducted to determine significant predictors of BMD. Results Athletes participating in low and moderate impact sports had significantly lower TB and site-specific BMD values when compared to high impact athletes (TB: High=1.24, Mod=1.18, Low=1.15; APS High=1.33, Mod=1.25, Low=1.21; DF High=1.25, Mod=1.12, Low=1.11). The BMD values for each secondary exposure were lower in those with history of abnormal menses (TB-1.19, APS- 1.24, DF- 1.17), previous stress fracture/reaction (TB-1.19, APS- 1.27, DF- 1.17), and poor family bone health (TB-1.18, APS- 1.26, DF- 1.16). Classification and regression trees (CART) analysis shows highest TB mean BMD was found in high impact athletes with LBM>50.9 kg. Conclusion There is an ordinal relationship between sport impact level and BMD. Using CART analysis, gynaecological age, impact level and LBM are the best predictors of mean BMD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".