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Record W2382713060

Application of MoCA in the screening of MCI in elderly patients

2014· article· en· W2382713060 on OpenAlexaboutno aff
Shao Ron

Bibliographic record

VenueZhiye yu jiankang · 2014
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitive impairmentMedicineCognitionInternal medicineCognitive Assessment SystemPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

[Objective]To explore the application of Montreal cognitive Assessment( MoCA) in the screening of mild cognitive impairment( MCI) in elderly patients.[Methods]A total of 56 MCI patients were selected as MCI group and 50 adults with normal cognition as control group. Their cognitive function was assessed according to the MoCA and MMSE scales. And the results were analyzed.[Results]The total score of MoCA was significantly lower than the total score of MMSE in MCI group and control group( P 0.01). The sensitivity and specificity of MoCA and MMSE were 96.4% and 84%,35.7% and 100%,respectively in MCI screening. The total MoCA and its sub-items were significantly different between the MCI group and the control group( P 0. 01),except for the fixed orientation( P 0. 01).[Conclusion]MoCA is a highly sensitive scale for MCI screening,which allows comprehensive assessment of the cognitive function of MCI patients,and it is more sensitive in screening MCI than MMSE.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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