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Cribado neurocognitivo en familiares de personas con deterioro cognitivo

2024· article· es· W4398757087 on OpenAlexaboutno aff
Julissa Mariela De León Rivas, Claudia Rocío González Joachín, Darinka Gabriela Cruz Cojulún

Bibliographic record

VenueRevista Académica Sociedad del Conocimiento Cunzac · 2024
Typearticle
Languagees
FieldMedicine
TopicAging, Health, and Disability
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychology

Abstract

fetched live from OpenAlex

PROBLEMA: el sedentarismo y/o baja actividad física está relacionado con casos de deterioro cognitivo leve en personas adultas, no asociados a la edad. OBJETIVO: determinar la relación entre el deterioro cognitivo leve, y el sedentarismo en adultos, familiares de pacientes geriátricos con deterioro cognitivo diagnosticado. MÉTODO: se eligieron familiares de pacientes geriátricos con deterioro cognitivo diagnosticado, en edades comprendidas entre los 30-60 años de edad, aplicándoseles el Cuestionario Internacional de Actividad Física para medir la actividad física y la evaluación cognitiva de Montreal para identificar deterioro cognitivo leve. RESULTADOS: no se encuentra relación significativa entre la baja actividad física y la aparición del deterioro cognitivo leve. CONCLUSIÓN: se concluye que no hay relación significativa entre deterioro cognitivo y baja actividad física, pero sí signos de alarma para la población entre 40-60 años en quienes se encontraron deterioro cognitivo leve, especialmente en el dominio de memoria, fluidez verbal, cálculo y habilidades visoespaciales, los cuales podrían estar asociados a una baja escolarización.

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)0.004
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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