Bone HealthAmongFemale Collegiate Athletes Participating in Loading and Active Loading Sports
Bibliographic record
Abstract
PURPOSE: Collegiate athletics are known to affect body composition, strength, and bone characteristics. However, it is unknown which sport is most beneficial for increased bone mineral density (BMD). To compare the BMD of female collegiate athletes (n=102) who compete in impact loading sports; ice hockey players (HP; n=24), cheerleading (CH; n=22), and ballet dancers (BD; N=10) to female athletes in active loading sports; synchronized swimmers (SS; n=20), and sedentary controls (SC; N = 26). METHODS: Participants underwent a total body, lumbar spine and femoral neck iDXA scan to evaluate BMD. Participants aged 20.5 ±1.8 years and weighed 61.3 ±9.8kg. ANCOVA compared BMD of the femoral neck, greater trochanter, total femoral, and lumbar spine (L1-L4) by sport while controlling for age. RESULTS: HP had the most regular menstrual cycles (83%), followed by SS (75%), SC (65%), CH (64%). BD had the least regular cycles (50%) and many were oligomenorrheic. BD and HP had a significantly higher BMD in the femoral neck and greater trochanter as compared to all other groups (p≤0.05; p≤0.001). However, HP had significantly higher total femoral BMD than CH, SS and SC. BD demonstrated higher femoral BMD compared to CH, SS and SC (p≤0.01). Analysis of the BMD in the lumbar spine revealed that HP and BD had significantly higher BMD as compared to the SS and SC (1.14±0.12g/cm2). Furthermore, there was no statistical difference between BD and the CH. However, CH had higher lumbar spine BMD than the SS and the SC (p≤0.00) (Table 1).Table 1: BMD by Sport TypeCONCLUSION: Although, ballet is an aesthetic sport as compared to hockey, both offer superior benefits to bone health than cheerleading, synchronized swimming, and being sedentary. The high prevalence of menstrual irregularity in the ballet dancers did not appear to negatively influence BMD. These results suggest that monitoring bone health in female athletes participating in low impact sports should be a priority.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".