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Record W2087911341 · doi:10.1080/17461391.2014.950345

Nutritional strategies to support concurrent training

2014· review· en· W2087911341 on OpenAlex
Joaquín Pérez‐Schindler, D. Lee Hamilton, Daniel R. Moore, Keith Baar, Andrew Philp

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Sport Science · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
FundersMedical Research Council
KeywordsTraining (meteorology)Physical medicine and rehabilitationMedicinePhysical therapyGeography

Abstract

fetched live from OpenAlex

Concurrent training (the combination of endurance exercise to resistance training) is a common practice for athletes looking to maximise strength and endurance. Over 20 years ago, it was first observed that performing endurance exercise after resistance exercise could have detrimental effects on strength gains. At the cellular level, specific protein candidates have been suggested to mediate this training interference; however, at present, the physiological reason(s) behind the concurrent training effect remain largely unknown. Even less is known regarding the optimal nutritional strategies to support concurrent training and whether unique nutritional approaches are needed to support endurance and resistance exercise during concurrent training approaches. In this review, we will discuss the importance of protein supplementation for both endurance and resistance training adaptation and highlight additional nutritional strategies that may support concurrent training. Finally, we will attempt to synergise current understanding of the interaction between physiological responses and nutritional approaches into practical recommendations for concurrent training.

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.

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.060
GPT teacher head0.340
Teacher spread0.280 · 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