Neuromuscular Fatigability Associated With Different Pacing Strategies During an Ultra-Endurance Pull-Up Task: A Case Study
Bibliographic record
Abstract
While neuromuscular fatigability has been previously characterized after running and cycling, no study has investigated an ultra-endurance upper body task. In preparation for a world record attempt, three pacing strategies to perform 1980 pull-ups in 6 hrs were compared during independent sessions: fast pace, long recovery (FL), fast pace, multiple short recoveries (FMS), and slow pace, no recovery (SN). Elbow flexion maximal voluntary contraction (MVC) force, grip strength, peripheral fatigue, and biceps brachii electromyography were quantified every 330 pull-ups and during recovery, alongside heart rate, perceived effort, and arm muscle pain. In all conditions, MVC force decreased rapidly within the first set of 330 pull-ups, with the greatest depression observed in FL (-29.1%) and more gradual declines in FMS (-18.6%) and SN (-8.6%). Similarly, FL displayed the greatest decline in potentiated single twitch (FL: -75.0%; FMS: -53.9%; SN: -41.8%) and high-frequency doublet forces (FL: -63.3%; FMS: -29.2%; SN: -41.8%) following the first set, as well as higher heart rate, effort, and pain throughout the task. Following 24 hrs, MVC force recovered slowest in FL and grip strength recovered fastest in SN. Therefore, for the world record attempt, a strategy with a continuous workload at slower pace should be used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".