Higher-protein intake improves body composition index in female collegiate dancers
Bibliographic record
Abstract
Aesthetic athletes strive to attain an ideal body image and the physical demands placed on dancers make their body composition and fitness equally as important as their technique. Body composition has shown positive changes in response to increased protein intake and may improve aesthetics of dance performance. The purpose of this study was to determine the extent to which supplemental whey protein (PRO) would improve body composition in female collegiate dancers compared with an isocaloric placebo (PLA). Twenty-one (age, 19.6 ± 1.4 years) female collegiate dancers were randomly assigned to consume PRO or PLA (25 g, 3×/day) for 12 weeks. Laboratory testing at weeks 0, 6, and 12 included 24-h urine collection, body composition (dual-energy X-ray absorptiometry), resting metabolic rate, and performance. Data were reported as means ± SD. Significance was accepted at p < 0.05. Body weight, fat mass, and lean soft tissue did not change between groups or over time. Body composition index (BCI = [(LSTpost − LSTpre) + (FMpre − FMpost)]; where LST is lean soft tissue, FM is fat mass, pre is pre-intervention, and post is post-intervention) significantly improved over time in PRO (+0.6 ± 1.9) but not PLA (−1.8 ± 3.1; p = 0.048); however, neither group demonstrated changes in laboratory performance tests. Protein supplementation for 12 weeks significantly improved BCI and provided a simple way to improve the diet in female collegiate dancers. Novelty Twelve weeks of protein supplementation does not change body weight in female collegiate dancers. BCI improves following protein supplementation in female collegiate dancers.
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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) | 0.001 | 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 | 0.000 | 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".