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Record W2767148378 · doi:10.1055/s-0043-119874

Ingesting A Sports Drink Enhances Simulated Ice Hockey Performance While Reducing Perceived Effort

2017· article· en· W2767148378 on OpenAlexaff
Matthew S. Palmer, George J. F. Heigenhauser, MyLinh Duong, Lawrence L. Spriet

Bibliographic record

VenueInternational Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of GuelphMcMaster UniversitySt. Francis Xavier University
Fundersnot available
KeywordsIce hockeySprintIngestionDehydrationGlycogenCore temperatureAnimal scienceMedicineMatch playCyclingPerceived exertionChemistryPhysical therapyInternal medicinePhysical medicine and rehabilitationBiologyBiochemistryBlood pressureHeart rate

Abstract

fetched live from OpenAlex

Abstract This study determined whether ingesting a carbohydrate-electrolyte solution (CES) vs. progressive dehydration affected skeletal muscle glycogen use and performance in ice hockey players during simulated ice hockey exercise comprised of 3 active “periods”. Seven males (21.3±0.3 years, 184.7±1.2 cm, 84.2±3.9 kg, and 49.6±1.8 mL·kg−1·min−1) performed a hockey-specific protocol on two occasions and either dehydrated progressively (NF), or stayed well-hydrated by ingesting a CES. Muscle biopsies were taken at rest, before the 3rd period (P3), and after the final sprint in the protocol. Compared to dehydration in the NF trial (−1.8% BM), CES ingestion enhanced voluntary performance (151.0±8.0 vs. 144.1±8.7 kJ) and glycogen use (177.5±31.1 vs. 103.5±16.2 mmol·kg dm−1), and reduced perceived exertion (16±1 vs. 18±1) in P3. Mean core temperature was reduced by CES ingestion throughout the protocol (38.0±0.2 vs. 38.1±0.1°C). These results suggest that compared to progressive dehydration, staying hydrated by ingesting a CES helps preserve performance, while reducing thermal and perceptual strains, in P3 of cycle-based simulation of ice hockey exercise. These benefits are observed despite greater glycogen use in P3 with CES ingestion.

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)0.001
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 score0.754
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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