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Record W3158867212 · doi:10.5007/1807-0221.2021.e69879

Efeito de dietas hiperproteicas nas adaptações musculares induzidas pelo treinamento resistido: revisão de literatura

2021· article· pt· W3158867212 on OpenAlexaboutno aff
Débora Kurrle Rieger, João Pedro Faraco, Bruna Cunha Mendes

Bibliographic record

VenueExtensio Revista Eletrônica de Extensão · 2021
Typearticle
Languagept
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineResistance trainingGynecologyPhysical therapy

Abstract

fetched live from OpenAlex

O objetivo do estudo foi analisar o efeito de dietas hiperproteicas nas adaptações musculares em indivíduos praticantes de treinamento resistido, assim como relatar possíveis alterações em dosagens bioquímicas de hormônios anabólicos. Foi realizada uma revisão da literatura nas bases de dados online PubMed, Scopus e Web of Science. Os estudos encontrados analisaram o efeito da ingestão de proteínas em quantidades a partir de 0,8 g/kg/dia até 4,4 g/kg/dia em períodos de duas a dezesseis semanas. Corroborando com a International Society of Sports Nutrition, Academy of Nutrition and Dietetics, Dietitians of Canada, American College of Sports Medicine e Nutrition guidelines for strentgh sports: Sprinting, weightlifting, throwing events, and bodybuilding, a presente revisão aponta que a ingestão de proteínas visando a hipertrofia muscular em praticantes de treinamento resistido, deve ser de aproximadamente 2 g/kg/dia. Quantidades acima deste valor não resultam em maior aumento de massa muscular em praticantes de treinamento resistido.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.009
Science and technology studies0.0000.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes1
Has abstractyes

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