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Record W3081288724 · doi:10.1590/2317-6431-2019-2272

Habilidades cognitivas e desempenho nos testes de ordenação temporal em idosos

2020· article· pt· W3081288724 on OpenAlexaboutno aff
Maysa Bastos Rabelo, Márcia da Silva Lopes, Ana Paula Corona, Jozélio Freire de Carvalho, Roberto Paulo Correia de Araújo

Bibliographic record

VenueAudiology - Communication Research · 2020
Typearticle
Languagept
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsStatistical significancePsychologyCognitionEffects of sleep deprivation on cognitive performanceMedicineGerontologyAudiologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

RESUMO Objetivo Investigar a influência dos domínios cognitivos no desempenho do teste padrão de frequência e teste padrão de duração em idosos. Métodos estudo seccional, desenvolvido em indivíduos com idade entre 60 e 79 anos. Realizou-se entrevista, avaliação cognitiva por meio do Montreal Cognitive Assessment, avaliação audiológica periférica (audiometria tonal e vocal) e central (teste padrão de frequência e teste padrão de duração). Resultados Participaram do estudo 58 mulheres com média de idade de 66 anos e 2 meses e 28 homens, com média de idade de 68 anos e 3 meses. Verificou-se que as habilidades visuoespacial, de atenção, concentração e memória de trabalho apresentaram correlação com os testes temporais no sexo feminino e que a habilidade de linguagem apresentou correlação com o teste padrão de frequência. Já entre os homens, houve tendência à significância quanto à capacidade visuoespacial. Ademais, as mulheres apresentaram melhor desempenho na habilidade de memória. Conclusão Os aspectos cognitivos podem influenciar nos testes de ordenação temporal em indivíduos idosos, sobretudo do sexo feminino.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.251
GPT teacher head0.442
Teacher spread0.190 · 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 designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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