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Record W4225397311 · doi:10.20396/conex.v20i00.8666856

Função cognitiva após exercícios aeróbicos com e sem restrição do fluxo sanguíneo em adultos mais velhos

2022· article· pt· W4225397311 on OpenAlexaff
Wellington Martins dos Santos, Amanda Veiga Sardeli, Daisa Fabiele Godoi Moraes, Marina Lívia Venturini Ferreira, Lucas do Carmo Santos, Cláudia Regina Cavaglieri, Laura E. Middleton, Mara Patrícia Traina Chacon‐Mikahil

Bibliographic record

VenueConexões · 2022
Typearticle
Languagept
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

As perdas cognitivas tipicamente experimentadas com o envelhecimento podem ser atenuadas por exercícios aeróbicos (EA) regulares. EA também induz melhora aguda da função cognitiva em idosos; no entanto, não está claro qual protocolo de EA é mais eficaz. A prática de EA com restrição de fluxo sanguíneo (RFS) agrega outros benefícios à saúde do idoso, como melhorias na aptidão aeróbia, aumento da massa e força muscular. Assim, objetivamos comparar protocolos de EA com e sem RFS na função cognitiva de idosos. Vinte e um idosos realizaram o teste de Stroop antes e após três sessões de EA em medida repetida, desenho cruzado: EA com alta carga (70% VO2máx), EA com baixa carga (40% VO2máx) e EA com RFS (40% VO2máx e 50% do RFS). Não houve efeito significativo das sessões experimentais na função cognitiva. Talvez, as cargas aplicadas não tenham sido adequadas para estimular melhorias na função cognitiva, visto que as cargas moderadas têm sido mais eficientes para aumentar o fluxo sanguíneo cerebral, entre outros mecanismos fisiológicos englobados. Além disso, observamos respostas bastante heterogêneas entre indivíduos e sessões, sugerindo que pesquisas futuras são necessárias para melhor compreensão desse fenômeno.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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