Estimating Muscle Fiber-Type Composition in Elite Athletes: A Survey on Current Practices and Perceived Merit
Bibliographic record
Abstract
PURPOSE: To gather information on practices and perceptions of high-performance experts regarding their athletes' muscle fiber-type composition (MFTC) and its estimation. METHODS: A questionnaire on the noninvasive versus invasive estimation of MFTC was completed by 446 experts including coaches and sport-science/sports-medicine staff. Moreover, the perceived importance of MFTC for training and performance optimization was assessed. Differences between sport types (individual and team sports) were analyzed using chi-square tests. RESULTS: Forty percent of the experts implemented MFTC assessment in pursuit of performance optimization, while 50% did not know their athletes' MFTC but expressed a desire to implement it if they would be able to assess MFTC. Ten percent did not perceive value in MFTC assessment. Only 18% of experts believed that their athletes would undergo a muscle biopsy, leading to the adoption of alternative noninvasive techniques. Experts primarily relied on their experience to estimate MFTC (65%), with experts working in individual sports using their experience more frequently than those working in team sports (68% vs 51%; P = .009). Jump tests emerged as the second-most commonly employed method for estimating MFTC (56%). When only considering experts who are currently using MFTC, 87% use MFTC to individualize training volume and 84% to individualize training intensity. CONCLUSIONS: Experts value MFTC assessment primarily to individualize training but mainly rely on noninvasive methods to estimate MFTC. Some of these methods lack scientific validity, suggesting a continuing need for education and further research in this area.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".