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Record W3002651847 · doi:10.1590/2237-6089-2018-0085

Sensitivity and specificity of the Brazilian version of the Montreal Cognitive Assessment – Basic (MoCA-B) in chronic kidney disease

2019· article· en· W3002651847 on OpenAlexaboutno aff
Thaís Malucelli Amatneeks, Amer Cavalheiro Hamdan

Bibliographic record

VenueTrends in Psychiatry and Psychotherapy · 2019
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCronbach's alphaMedicineCognitionKidney diseaseHemodialysisPhysical therapyInternal medicineCognitive impairmentGerontologyClinical psychologyPsychiatryPsychometrics

Abstract

fetched live from OpenAlex

INTRODUCTION: Cognitive impairment in chronic kidney disease (CKD) is commonly associated with neuropsychiatric disorders. As a complex pathology, at all stages of CKD patients need to have a good understanding of the need for drug and nutritional adherence. Cognitive screening is the starting point for detection of cognitive impairments. OBJECTIVE: To determine the specificity and sensitivity of the Brazilian Portuguese version of the Montreal Cognitive Assessment - Basic (MoCA-B) for identification of cognitive impairment in the CKD population. METHODS: This was a cross-sectional study with 163 CKD patients undergoing hemodialysis treatment. The Mini-Mental State Examination (MMSE) and MoCA-B were administered. RESULTS: The MoCA-B has reliable internal consistency (Cronbach's alpha = 0.74). A cutoff point of ≤ 21 points provides the best sensitivity and specificity for detection of cognitive impairment. The education variable had less impact on the total MoCA-B score than on the total MMSE score. CONCLUSIONS: The MoCA-B is a suitable screening instrument for evaluating the global cognition of hemodialysis patients. The results can help health professionals to conduct evaluations and plan clinical management.

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)0.000
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 score0.044
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.286
Teacher spread0.278 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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Same venueTrends in Psychiatry and PsychotherapySame topicDialysis and Renal Disease ManagementFrench-language works237,207