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Record W2186056962 · doi:10.3389/fphys.2015.00354

Thermal and Cardiovascular Strain Mitigate the Potential Benefit of Carbohydrate Mouth Rinse During Self-Paced Exercise in the Heat

2015· article· en· W2186056962 on OpenAlexaff
Matthew N. Cramer, Martin W. Thompson, Julien D. Périard

Bibliographic record

VenueFrontiers in Physiology · 2015
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHeart rateTime trialMedicineCardiac outputPlaceboPerceived exertionCardiologyAnimal scienceInternal medicineStroke volumeCarbohydrateBlood pressureBiology

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether a carbohydrate mouth rinse can alter self-paced exercise performance independently of a high degree of thermal and cardiovascular strain. METHODS: Eight endurance-trained males performed two 40-km cycling time trials in 35°C, 60% RH while swilling a 20-ml bolus of 6.5% maltodextrin (CHO) or a color- and taste-matched placebo (PLA) every 5 km. Heart rate, power output, rectal temperature (Tre), and mean skin temperature (Tsk) were recorded continuously; cardiac output, oxygen uptake (VO2), mean arterial pressure (MAP), and perceived exertion (RPE) were measured every 10 min. RESULTS: Performance time and mean power output were similar between treatments, averaging 63.9 ± 3.2 and 64.3 ± 2.8 min, and 251 ± 23 and 242 ± 18 W in CHO and PLA, respectively. Power output, stroke volume, cardiac output, MAP, and VO2 decreased during both trials, increasing slightly or remaining stable during a final 2-km end-spurt. Tre, Tsk, heart rate, and RPE increased throughout exercise similarly with both treatments. Changes in RPE correlated with those in Tre (P < 0.005) and heart rate (P < 0.001). CONCLUSIONS: These findings suggest that carbohydrate mouth rinsing does not improve ~1-h time trial performance in hot-humid conditions, possibly due to a failure in down-regulating RPE, which may be influenced more by severe thermal and cardiovascular strain.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.317

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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 designObservational
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

Citations26
Published2015
Admission routes1
Has abstractyes

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