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Record W2369659353

Application of Montreal cognitive assessment in the patients with Parkinson's disease

2011· article· en· W2369659353 on OpenAlexaboutno aff
Peng Kai-run

Bibliographic record

VenueJournal of Apoplexy and Nervous Diseases · 2011
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionParkinson's diseaseCognitive impairmentDiseaseMedicinePsychologyPhysical medicine and rehabilitationInternal medicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective To analyze the characteristics of cognitive function impairment in the patients with Parkinson's disease(PD) by Chinese version of Montreal cognitive assessment(MoCA) and to explore the application of MoCA in the patients with PD.Methods Thirty-five patients with PD were detected by MoCA and the results were analyzed.Results The total score of MoCA in patients with PD were 20.51±5.767,and there were seven patients less than 26.The incidences were different in the impairment of each cognitive domain of PD.It was indicated by the relevance analysis between each cognitive domain and total score in MoCA that attention,visual space and executive capacity were relevant closely with the total score in the scale.Conclusion MoCA could be used as a tool in clinical study of cognitive function for PD.Cognitive domain damage of PD were language,memory,visual space and executive capacity,Abstraction and attention.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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
Published2011
Admission routes1
Has abstractyes

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