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Record W4399729898 · doi:10.1101/2024.06.16.24308987

High orientation and low delayed recall in the standardisation of the Spanish version of the Montreal Cognitive Assessment (MoCA) in elders of Peru

2024· preprint· en· W4399729898 on OpenAlexaboutno aff
Lucia Bulgarelli, Emilia Gyr, Jose Villanueva, Koni K. Mejía, Claudia Mejía, Renato Paredes, Sheyla Blumen

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsnot available
FundersPontificia Universidad Católica del Perú
KeywordsRecallOrientation (vector space)PsychologyCognitive psychologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION The elderly in Peru face healthcare barriers in detecting cognitive impairment and dementia due to a lack of validated tools. The Montreal Cognitive Assessment (MoCA) effectively detects early dementia, assessing visuo-spatial function, executive function, naming, memory, attention, language, abstraction, and orientation. METHODS This study aims to standardise the Spanish version of the MoCA for the elderly in Lima. The test was administered to 338 elders from three institutions: Municipality of San Miguel District, San José Obrero Polyclinic, and EDMECON. Regression-based normed scores were computed, adjusted for age and education. RESULTS Our results show high orientation scores and low delayed recall, highlighting cognitive strengths and weaknesses in our sample. Age and education significantly influenced cognitive performance, with education as the strongest predictor. DISCUSSION This study offers normative data for the Peruvian elderly, aiding the clinical use of MoCA in Peru. We discuss appropriate cut-off points and cultural sensitivity in the Peruvian context.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.340
Teacher spread0.322 · 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 teacher head, 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

Citations0
Published2024
Admission routes1
Has abstractyes

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