Peripheral fatigue is not critically regulated during maximal, intermittent, dynamic leg extensions
Bibliographic record
Abstract
Central motor drive to active muscles is believed to be reduced during numerous exercise tasks to prevent excessive peripheral fatigue development. The purpose of the present study was to use hypoxia to exacerbate physiological perturbations during a novel, intermittent exercise task and to explore the time-course and interplay between central and peripheral neuromuscular adjustments. On separate days, 14 healthy men performed four sets of 6 × 5 maximal-intensity, isokinetic leg extensions (1 repetition lasting ∼7 s) at 300°/s (15 and 100 s of passive rest between repetitions and sets, respectively) under normoxia (NM, fraction of inspired O2 0.21), moderate (MH, 0.14), and severe normobaric hypoxia (SH, 0.10). Neuromuscular assessments of the knee extensors were conducted before and immediately after each set. There was an interaction between time and condition on the mean peak torque produced during each set (P < 0.05). RMS/M-wave activity of the rectus femoris decreased across the four sets of exercise, but there was no difference between conditions (8.3 ± 5.1% all conditions compounded, P > 0.05). Potentiated twitch torque decreased post set 1 in all conditions (all P < 0.05) with greater reductions following each set in SH compared with NM but not MH (end-exercise reductions 41.3 ± 3.0% vs. 28.0 ± 3.2%, P < 0.05 and 32.1 ± 3.3%, P > 0.05). In conclusion, severe hypoxia exacerbates both peripheral fatigue development and performance decrements during maximal, intermittent, dynamic leg extensions. In contrast to observations with other exercise modes, during exercise involving a single muscle group the attenuation of central motor drive does not appear to independently regulate the development of peripheral muscle fatigue.
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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 | 0.000 |
| 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) | 0.001 | 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".