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Record W2414157483 · doi:10.1249/jsr.0000000000000180

Nutrition to Support Recovery from Endurance Exercise

2015· article· en· W2414157483 on OpenAlexaff
Daniel R. Moore

Bibliographic record

VenueCurrent Sports Medicine Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePhysical therapyPain medicineSports medicineEndurance trainingSports nutritionPhysical medicine and rehabilitationIntensive care medicineAthletesAnesthesiologyAnesthesia

Abstract

fetched live from OpenAlex

Proper nutrition is vital to optimize recovery after endurance exercise. Dietary carbohydrate and protein provide the requisite substrates to enhance glycogen resynthesis and remodel skeletal muscle proteins, respectively, both of which would be important to rapidly restore muscle function and performance. With short recovery windows (<8 h), coingestion of these macronutrients immediately after exercise can synergistically enhance glycogen resynthesis and rapidly stimulate muscle protein synthesis (MPS), the latter of which is augmented by protein ingestion alone. Consuming frequent meals throughout the day containing adequate carbohydrate (according to training intensity) and protein (approximately 0.25 g·kg) will help fully restore muscle glycogen and sustain maximal daily rates of MPS over prolonged (8 to 24 h) recovery periods. Given the complementarity of these macronutrients, endurance athletes aiming to maximize postexercise recovery to maintain or enhance subsequent exercise performance should target a nutrition strategy that features optimal ingestion of both carbohydrate and protein.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.816

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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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 designNot applicable
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

Citations62
Published2015
Admission routes1
Has abstractyes

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