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Record W1962368836 · doi:10.1123/ijsnem.12.2.136

The Effect of Pre-exercise Glucose Ingestion on Performance during Prolonged Swimming

2002· article· en· W1962368836 on OpenAlexaff
Gareth J. Smith, Edward C. Rhodes, R. H. Langill

Bibliographic record

VenueInternational Journal of Sport Nutrition and Exercise Metabolism · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIngestionPlaceboMedicineHeart rateAnimal scienceStatistical significanceAnesthesiaInternal medicineChemistryBlood pressureBiology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine if pre-exercise glucose ingestion would improve distance swimming performance. Additionally, pre-exercise glucose was provided at 2 different feeding intervals to investigate the affects of the timing of administration. Ten male triathletes (mean +/- SD: age, 29.5 +/- 5.0 years; VO2peak, 48.8 +/- 3.2 ml.kg-1.min-1) swam 4000 m on 3 occasions following the consumption of either a 10% glucose solution 5 min prior to exercise (G5), a 10% glucose solution 35 min prior to exercise (G35), or a similar volume of placebo (PL). Despite a significant difference (p < .01) in blood glucose concentration prior to exercise (mean +/- SD in mmol.L-1: G35 8.4 +/- 1.1 vs. G5 5.2 +/- 0.5 or PL 5.3 +/- 0.4), no significant differences were observed in total time (mean +/- SD in minutes: G35 70.7 +/- 7.6, G5 70.1 +/- 7.6, PL 71.9 +/- 8.4), post-exercise blood glucose (mean +/- SD in mmol.L-1: G35 5.1 +/- 1.1, G5 5.1 +/- 0.9, PL 5.3 +/- 0.4), and average heart rate (mean +/- SD in bpm: G35 155.8 +/- 10.8, G5 153.6 +/- 12.6, PL 152.0 +/- 12.5; p > .05). While not reaching statistical significance, glucose feedings did result in improved individual performance times, ranging from 24 s to 5 min in 8 of the 10 subjects compared to the placebo. These results were found despite significant differences in blood glucose between trials immediately prior to exercise.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 designOther design
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

Citations18
Published2002
Admission routes1
Has abstractyes

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