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Record W7100649828

Research Article Montreal Cognitive Assessment Is Superior to Standardized Mini-Mental Status Exam in Detecting Mild Cognitive Impairment in the Middle-Aged and Elderly Patients with Type 2 Diabetes Mellitus

2016· article· en· W7100649828 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDementiaCognitive impairmentType 2 diabetesType 2 Diabetes MellitusCognitionDepression (economics)Cohen's kappaProspective cohort study
DOInot available

Abstract

fetched live from OpenAlex

Copyright © 2013 Kannayiram Alagiakrishnan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Aim. This study compares the usefulness of Montreal Cognitive Assessment (MoCA) to Standardized Mini-Mental Status Exam (SMMSE) for diagnosingmild cognitive impairment (MCI) in Type 2 diabetesmellitus (DM) population.Methods.This prospective pilot study enrolled 30 community dwelling adults with Type 2 DM aged 50 years and above. Subjects were assessed using both the SMMSE and MoCA for MCI. In all subjects, depression and dementia were ruled out using the DSM IV criteria, and a functional assessment was done. MCI was diagnosed using the standard test, the European consortium criteria. Sensitivity and specificity analysis, positive and negative predictive values, likelihood ratios and Kappa statistic were calculated. Results. In comparison to consortium criteria, the sensitivity and specificity of MoCA were 67 % and 93 % in identifying individuals with MCI, and SMMSE were 13 % and 93%, respectively. The positive and negative predictive values for MoCA were 84 % and 56%, and for SMMSE were

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.279
Teacher spread0.237 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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