Fatigue in elite fencing: Effects of a simulated competition
Bibliographic record
Abstract
The fatigue induced by fencing remains scarcely investigated. We aimed to investigate both objective (neuromuscular performance fatigability) and subjective (perceived fatigue, effort, and workload) manifestations of fatigue in elite fencers following a five-bout simulated competition. Changes in countermovement jump height, knee extensors maximal isometric torque, rate of torque development, voluntary activation, and contractile response to muscular electrical stimulation were measured in 29 elite fencers [12 epee (6 women), 11 saber (5 women), and 6 foil]. Perceived fatigue and effort were evaluated with visual analog scales, and the perceived workload with the NASA Task Load Index scale. During the competition, maximal torque and rate of torque development decreased by 1.6% (p = 0.017) and 2.4% (p < 0.001) per bout, respectively. Perceived fatigue before each bout increased (12% per bout), with similar values observed at the end of all bouts (bout × period interaction: p < 0.001). Perceived effort increased during the bouts (10% per period, p < 0.001) and during the competition (3% per bout, p = 0.011). Perceived mental demand increased during the competition (2% per bout, p = 0.024). These results suggest that elite fencers needed to increase the allocation of mental rather than physical resources to the task to counterbalance the deleterious effect of fatigue on performance.
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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 | 0.001 |
| 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) | 0.001 | 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".