Effect of a carbohydrate mouth rinse on simulated cycling time-trial performance commenced in a fed or fasted state
Bibliographic record
Abstract
It is presently unclear whether the reported ergogenic effect of a carbohydrate (CHO) mouth rinse on cycling time-trial performance is affected by the acute nutritional status of an individual. Hence, the aim of this study was to investigate the effect of a CHO mouth rinse on a 60-min simulated cycling time-trial performance commenced in a fed or fasted state. Twelve competitive male cyclists each completed 4 experimental trials using a double-blinded Latin square design. Two trials were commenced 2 h after a meal that contained 2.5 g·kg(-1) body mass of CHO (FED) and 2 after an overnight fast (FST). Prior to and after every 12.5% of total time during a performance ride, either a 10% maltodextrin (CHO) or a taste-matched placebo (PLB) solution was mouth rinsed for 10 s then immediately expectorated. There were significant main effects for both pre-ride nutritional status (FED vs. FST; p < 0.01) and CHO mouth rinse (CHO vs. PLB; p < 0.01) on power output with an interaction evident between the interventions (p < 0.05). The CHO mouth rinse improved mean power to a greater extent after an overnight fast (282 vs. 273 W, 3.4%; p < 0.01) compared with a fed state (286 vs. 281 W, 1.8%; p < 0.05). We concluded that a CHO mouth rinse improved performance to a greater extent in a fasted compared with a fed state; however, optimal performance was achieved in a fed state with the addition of a CHO mouth rinse.
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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) | 0.001 | 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) | 0.002 | 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".