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Record W2382713060

Application of MoCA in the screening of MCI in elderly patients

2014· article· en· W2382713060 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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