Neuromuscular Fatigue of Cycling Exercise in Hypoxia
Bibliographic record
Abstract
INTRODUCTION: The understanding of fatigue in hypoxia is limited due to: lack of control in arterial saturation, different exercise intensities and hypoxia levels, lag time between exercise cessation and fatigue evaluation. We aimed at evaluating fatigue during cycling and immediately after exhaustion (EXH) in normoxia, moderate and severe hypoxia at relative and absolute intensities. METHODS: Thirteen subjects completed three sessions in normoxia, moderate, and severe hypoxia with intensity based on percentage of normoxic maximal power output (NOR, MODABS, SEVABS) plus two sessions where intensity was based on the corresponding environmental condition (MODREL, SEVREL). Arterial saturation was clamped at 85% and 70% in moderate and severe hypoxia, respectively. Before, during cycling, and at EXH, maximum voluntary contraction (MVC), peripheral fatigue (high-frequency doublet [Db100], twitch [Pt]), and central fatigue (cortical voluntary activation [VATMS]) were evaluated without delay using an innovative ergometer. RESULTS: Time to EXH declined not only with hypoxia level at absolute but also relative intensities compared to NOR. At isotime, MVC, Pt, and Db100 were similarly depreciated in NOR, MODREL, and SEVREL. At EXH, there was a similar reduction among conditions in MVC (-26% to -31%), Db100 (-25% to -35%) and VATMS (-9% to -13%). However, Pt was less decreased in SEVREL compared with NOR (-33% ± 17% vs -46% ± 16%). CONCLUSIONS: The shorter time to EXH in relative hypoxia and yet lower peripheral fatigue and similar central fatigue compared with normoxia suggests that hypoxia per se may affect brain areas not directly implicated in quadriceps motor function.
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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) | 0.003 | 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".