Mouth rinsing improves cycling endurance performance during Ramadan fasting in a hot humid environment
Bibliographic record
Abstract
This study examined the effect of mouth rinsing during endurance cycling in a hot humid environment (32 °C and 75% relative humidity) on athletes in the Ramadan fasted state. Nine trained adolescent male cyclists completed 3 trials that consisted of a carbohydrate mouth-rinse (CMR), a placebo mouth-rinse (PMR), and a no-rinse (NOR) trial during the last 2 weeks of Ramadan. Each trial consisted of a preloading cycle at 65% peak rate of oxygen consumption for 30 min followed by a 10-km time trial (TT10 km) under hot humid condition. During the CMR and PMR trials, each cyclist rinsed his mouth with 25 mL of the solution for 5 s before expectorating the solution pre-exercise, after 5, 15, and 25 min of the preloading cycle, and 15 s prior to the start of TT10 km. Time to complete the TT10 km was significantly faster in the CMR and PMR trials compared with the NOR trial (12.9 ± 1.7 and 12.6 ± 1.7 vs. 16.8 ± 1.6 min, respectively; p < 0.017). Ratings of perceived exertion taken at the end of the TT10 km was lower in both CMR and PMR trials compared with the NOR trial, although the difference was significant only between CMR and NOR (p < 0.05). In conclusion, mouth rinsing with either carbohydrate or placebo solution provided ergogenic benefits compared with a no-rinse condition on TT10 km performance in acute Ramadan fasted subjects during endurance cycling in a heat stress environment.
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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) | 0.001 | 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".