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Record W4413073943 · doi:10.24875/rene.m25000015

Relation of cognitive functions and activities of daily living in older adults with diabetes

2025· article· en· W4413073943 on OpenAlexaboutno aff
Yarely Y. Zurita-López, Rosa María Galicia-Aguilar, Erick Landeros-Olvera, Erika Lozada‐Perezmitre

Bibliographic record

VenueRevista de enfermería neurológica (English ed ) · 2025
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyRelation (database)Activities of daily livingCognitionDiabetes mellitusPsychologyMedicinePsychiatryComputer scienceEndocrinology

Abstract

fetched live from OpenAlex

Introduction: Aging is a continuous, multifaceted, and irreversible process that leads to a gradual decline in physical, cognitive, and social abilities, alongside an increased risk of disease.Type 2 diabetes can induce metabolic changes that may impact cognitive functions and activities of daily living (ADL) in older adults.Objective: To examine the relationship between cognitive functions and ADL in older adults with type 2 diabetes at a health center in Oaxaca.Method: This quantitative, correlational, descriptive, and cross-sectional study used a finite population formula to determine the sample size, resulting in 277 older adults.Two assessment tools were employed: the Montreal Cognitive Assessment Test and the Barthel Index.Results: The study population was predominantly female (n = 181), with a mean age of 67.17 years (SD = 6.2).A probable cognitive disorder was reported in 53.2% (n = 147) of participants, while 83% (n = 230) remained independent in ADL.A statistically significant positive correlation (r = 0.396, p = 0.000) was found between cognitive function and ADL.Conclusions: The findings underscore the association between cognitive function and ADL in older adults with type 2 diabetes.

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)0.002
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 score0.013
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.270
Teacher spread0.261 · 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
Published2025
Admission routes1
Has abstractyes

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