The effect of a carbohydrate mouth-rinse on neuromuscular fatigue following cycling exercise
Bibliographic record
Abstract
Carbohydrate (CHO) mouth-rinsing, rather than ingestion, is known to improve performance of high-intensity (>75% maximal oxygen uptake) short-duration (≤1 h) cycling exercise. Mechanisms responsible for this improvement, however, are unclear. The present study aimed to investigate the effect of a CHO mouth-rinse on cycling time-trial (TT) performance and mechanisms of fatigue. On 2 separate occasions, 9 male cyclists (mean ± SD; maximal oxygen uptake, 61 ± 5 mL·kg(-1)·min(-1)) completed 45 min at 70% maximum power output (preload) followed by a 15-min TT. At 7.5-min intervals during the preload and TT, participants were given either a tasteless 6.4% maltodextrin mouth-rinse (CHO) or water (placebo (PLA)) in a double-blind, counterbalanced fashion. Isometric knee-extension force and electromyographic responses to percutaneous electrical stimulation and transcranial magnetic stimulation were measured before, after the preload, and after the TT. There were greater decreases in maximal voluntary contraction after the TT in PLA (20% ± 10%) compared with the CHO (12% ± 8%; P = 0.019). Voluntary activation was reduced following exercise in both trials, but did not differ between conditions (PLA -10% ± 8% vs. CHO -5% ± 4%; P = 0.150). The attenuation in the manifestation of global fatigue did not translate into a TT improvement (248 ± 23 vs. 248 ± 39 W for CHO and PLA, respectively). Furthermore, no differences in heart rate or ratings of perceived exertion were found between the 2 conditions. These data suggest that CHO mouth-rinsing attenuates neuromuscular fatigue following endurance cycling. Although these changes did not translate into a performance improvement, further investigation is required into the role of CHO mouth-rinse in alleviating neuromuscular 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".