Can Collegiate Hockey Players Accurately Predict Regional and Total Body Physiologic Changes throughout the Competitive Season?
Bibliographic record
Abstract
A collegiate athlete’s body composition can fluctuate due to factors such as nutrition, sleep, and training load. As changes in body composition can affect an athlete’s level of performance, it may be beneficial if athlete’s can accurately predict these changes throughout a season. The purpose of this study was to determine how well a group of 23 male collegiate hockey players (age = 22.44 ± 1.16 years, height = 181.30 ± 6.99 cm, weight = 86.41 ± 8.32 kg) could predict their regional and total body lean and fat tissue mass throughout a hockey season (September to March). Total body, trunk, lower body, and upper body compositional changes were measured at the beginning and at the end of the competitive season using dual energy X-Ray absorptiometry (DXA). At the end of the season, a questionnaire was completed by each participant to explore how they perceived their body composition changes (losses or gains in lean tissue and fat mass) throughout the season. Overall, players had a difficult time identifying actual changes in lean tissue and fat mass throughout the season. Upper body fat and lean tissue changes were perceived most accurately, while perceptions of body fat were related to android adiposity but not visceral adiposity. These findings suggest that some regional areas of body composition changes may happen without being noticed. For strength and conditioning coaches, if athletes are made aware of these changes before they become exaggerated, proper dietary, and training adaptations can be made to enhance performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".