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Record W2080487808 · doi:10.1139/h07-091

Glutamine and carbohydrate supplements reduce ammonemia increase during endurance field exercise

2007· article· en· W2080487808 on OpenAlexvenueno aff
Jacqueline Carvalho-Peixoto, Robson Cardilo Alves, Luiz Cláudio Cameron

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsGlutamineAthletesEndurance trainingBolus (digestion)CarbohydrateMedicineInternal medicineAnimal scienceAmmoniaElite athletesEndocrinologyPhysical therapyChemistryBiochemistryBiologyAmino acid

Abstract

fetched live from OpenAlex

Blood ammonia concentration increases during endurance exercise and has been proposed as a cause for both peripheral and central fatigue. We examined the impact of glutamine and (or) carbohydrate supplementation on ammonemia in high-level runners. Fifteen men in pre-competitive training ran 120 min (approximately 34 km) outdoors on 4 occasions. On the first day, the 15 athletes ran without the use of supplements and blood samples were taken every 30 min. After that, each day for 4 d before the next 3 exercise trials, we supplemented the athletes' normal diets in bolus with carbohydrate (1 g.kg(-1).d(-1)), glutamine (70 mg.kg(-1).d(-1)), or a combination of both in a double-blind study. Blood ammonia level was determined before the run and every 30 min during the run. During the control trial ammonia increased progressively to approximately 70% above rest concentration. Following supplementation, independent of treatment, ammonia was not different (p>0.05) for the first 60 min, but for the second hour it was lower than in the control (p<0.05). Supplementation in high-level, endurance athletes reduced the accumulation of blood ammonia during prolonged, strenuous exercise in a field situation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.915

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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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

Citations73
Published2007
Admission routes1
Has abstractyes

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