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Record W2318538829 · doi:10.1139/apnm-2013-0276

Mouth rinsing improves cycling endurance performance during Ramadan fasting in a hot humid environment

2013· article· en· W2318538829 on OpenAlexvenueno aff
Ahmad Munir Che Muhamed, Norjana Ismail, Abdul Rashid Aziz, Rabindarjeet Singh

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2013
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsCyclingPlaceboMedicineTime trialPerceived exertionMouth rinseHeat stressAnimal sciencePhysical therapyHeart rateInternal medicineDentistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.226
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2013
Admission routes1
Has abstractyes

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