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Record W4401646279 · doi:10.1590/1806-9282.20240429

Assessment of cognitive function in elderly patients with heart failure

2024· article· pt· W4401646279 on OpenAlexaboutno aff
Leandro Marques da Silva, Carla Priscilla Belchior Marques Sampaio, Nicolly Thomas Guimarães, L Moreno, Gedean Souza Pontes, E. Ferreira, José Albuquerque de Figueiredo Neto

Bibliographic record

VenueRevista da Associação Médica Brasileira · 2024
Typearticle
Languagept
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsHeart failureCognitionMedicinePsychologyCardiologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) tests for the identification of cognitive deficit (CD) in elderly patients with heart failure (HF). METHODS: This was a cross-sectional study with an observational design involving 43 elderly patients with HF of both sexes, treated by the Unified Health System, who were able to understand and follow the study instructions. A sociodemographic and clinical questionnaire and the MMSE and MoCA neurocognitive tests were applied. RESULTS: The mean age of the patients was 67 years; 67.44% were male; 53.49% were white; 58.14% had 1-4 years of schooling; 58.14% had an income of half to one minimum wage; 55.81% were married; 53.49% had a family history of HF; 90.7% denied smoking; 83.72% denied alcohol intake; 65.12% did not practice physical activity; 83.72% were hypertensive; 30.23% were diabetic; 57.89% had LVEF ≥ 50%; 39.53% have NYHA II; and 88.37% did not have a pacemaker. In the identification of CD, the MMSE test detected it in 25.58% of the patients, while the MoCA test identified it in 23.26% (p=0.043). CONCLUSION: It was concluded that the MMSE test performed better than the MoCA test in the identification of CD in elderly patients with HF.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.326
Teacher spread0.309 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRevista da Associação Médica BrasileiraSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207