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Record W6980728593

Concordancia entre las pruebas Mini Mental State Examination, Short Portable Mental Status Questionnarie y Montreal Cognitive Assesment para el tamizaje del deterioro cognitivo en adultos mayores

2020· dissertation· en· W6980728593 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2020
Typedissertation
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsConcordanceMontreal Cognitive AssessmentCognitive impairmentMini–Mental State ExaminationCohortCognitionTest (biology)Cohort study
DOInot available

Abstract

fetched live from OpenAlex

Objective: The objective of the study is to determine the level of concordance between the MMSE, SPMSQ and MoCA tests for the screening of cognitive impairment in older adults through the Kappa index between the three tests.
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\nMaterial and methods: Retrospective cohort study in people attended in the Geriatrics service of the Naval Medical Center "Cirujano Mayor Santiago Távara", selected by convenience. A total of 1683 patient were included, taking as points of cut to determine cognitive deterioration a score higher than 4 in SPMSQ; a score lower than 26 in MoCA; and a score lower than 25 in MMSE. Cohen's Kappa Index was used, using a value of 0.8 as an indicator of good concordance between them.
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\nResults: The MMSE was the test that found the largest number of patients with cognitive impairment, giving 43.32% of the total. A good level of concordance between the MMSE and MoCA tests was observed (Kappa index: 0.99 IC95% 0.99 - 1.00 p <0.01), and a discordant result between the MoCA and SPMSQ tests (Kappa index: 0.42 95% CI 0.38 - 0.46 p <0.01); and the MMSE and SPMSQ tests (Kappa index: 0.42 IC95% 0.38 - 0.46 p <0.01)
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\nConclusion: The MoCA and MMSE tests maintain an excellent concordance between them. While, the result of the SPMSQ test is discordant with respect to the others (MMSE / MoCA).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.066
GPT teacher head0.398
Teacher spread0.333 · 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
Published2020
Admission routes1
Has abstractyes

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