Do Canadian collegiate hockey players accurately perceive body composition changes after unmonitored training and diet?
Bibliographic record
Abstract
Collegiate athletes often use nutritional programs and supplements to elicit body composition changes in muscle or fat. It is unknown if athletes can accurately perceive their fluctuations in body composition, yet their understanding may help them make more accurate interpretations regarding the success of potential nutrition or exercise regimens. The purpose of this study was to investigate if collegiate hockey players could accurately perceive a change in body composition during a 3-month period within their regular season, in which no predetermined nutritional or exercise program was provided. Twenty-four male Canadian collegiate hockey players completed preseason and midseason body composition assessments using dual-energy X-ray absorptiometry. Immediately before the midseason scan, players attempted to accurately match their perceived fluctuation in composition, with predetermined categorical ranges of relative body composition and strength. Two-thirds of players and one-half of players accurately perceived changes in arm-lean and arm-fat tissue, respectively. Approximately two-thirds of players did not accurately perceive gains or losses of lean or fat tissue within their leg and overall body. Although some athletes partially detected changes in the lean and fat tissue of particular regions, the vast majority of players cannot detect the type, or amount of tissue gained and lost across the overall body. Body composition assessments, rather than an athlete's perceptions, should be used to help interpret the success of a sport nutrition or exercise program. Athletes should be aware that physiologic adaptations might take place unnoticed, which could affect the acceptance and adherence of nutrition or exercise interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".