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Record W2469638311 · doi:10.1002/gps.4539

Montreal Cognitive Assessment and Mini-Mental State Examination reliable change indices in healthy older adults

2016· article· en· W2469638311 on OpenAlexaboutno aff
Miloslav Kopeček, Ondřej Bezdíček, Zdeněk Šulc, Jiří Lukavský, Hana Štěpánková

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute of Mental HealthEuropean Regional Development FundNational Institutes of HealthMinisterstvo Zdravotnictví Ceské Republiky
KeywordsMontreal Cognitive AssessmentMini–Mental State ExaminationGerontologyCognitionPsychologyMental stateMedicineCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Cognitive tests are used repeatedly to assess the treatment response or progression of cognitive disorders. The Montreal Cognitive Assessment (MoCA) is a valid screening test for mild cognitive impairment. The aim of our study was to establish 90% reliable change indices (RCI) for the MoCA together with the Mini-Mental State Examination (MMSE) in cognitively healthy older adults. METHOD: We analyzed 197 cognitively healthy and functional independent volunteers aged 60-94 years, who met strict inclusion criteria for four consecutive years. The RCI methods by Chelune and Hsu were used. RESULTS: For 1, 2, and 3 years, the 90% RCI for MoCA using Chelune's formula were -4 ≤, ≥4; -4 ≤, ≥4 and -5 ≤, ≥4 points, respectively, and -3 ≤, ≥3 for the MMSE each year. Ninety percent RCI for MoCA using Hsu's formula ranged from -6 to 0, respectively, and +3 to +8 dependent on the baseline MoCA. CONCLUSION: Our study demonstrated RCI for the MoCA and MMSE in a 3-year time period that can be used for the estimation of cognitive decline or improvement in clinical settings. Copyright © 2016 John Wiley & Sons, Ltd.

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 score0.195
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.342
Teacher spread0.329 · 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

Citations60
Published2016
Admission routes1
Has abstractyes

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