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Record W2387494524

Incidence and related factors of Parkinson' s disease with cognitive dysfunction

2014· article· en· W2387494524 on OpenAlexaboutno aff
Zhao Pen

Bibliographic record

VenueJournal of Zhengzhou University · 2014
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionIncidence (geometry)Parkinson's diseaseInternal medicineDiseaseExecutive dysfunctionMedicineLogistic regressionMontreal Cognitive AssessmentPsychologyCognitive impairmentPsychiatryNeuropsychology
DOInot available

Abstract

fetched live from OpenAlex

Aim: To study the incidence,clinical characteristics and related factors of cognitive dysfunction in Parkinson's disease( PD). Methods: A total of 52 patients with Parkinson's disease( PD group) and 58 healthy patients( normal group) were assessed by Montreal cognitive assessment( MoC A) scale,to study the incidence of cognitive dysfunction,clinical characteristics in PD patients and the relationship between the PD patients with cognitive dysfunction and age,duration,Hoehn-Yahr grade,UPDRS score. Results: The MoC A score in PD group was lower than that in normal group( t =8. 036,P 0. 001),the incidence of cognitive dysfunction was 57. 7% in the PD group( χ2= 19. 200,P 0. 001). The main features of cognitive dysfunction in PD patients showed attention and concentration,executive functions,memory,delayed recall,language skills,visual ability,abstract thinking and ability of orientation,retardation,etc. Logistic regression analysis showed that cognitive dysfunction in PD group was positively related with the severity of disease condition( H-Y classification),duration,UPDRS( Part Ⅰ) score and UPDRS( Part Ⅱ) score( OR: 5. 464,6. 172,1. 138,1. 305; 95%CI: 1. 915- 10. 136,0. 068- 0. 872,1. 017- 1. 358,1. 316- 11. 014). Conclusion: The incidence of cognitive dysfunction in PD patients is high. There are many forms of clinical manifestations,and the extent of these manifestations is correlated with disease duration,severity and UPDRS score.

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)0.000
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 score0.003
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of Zhengzhou UniversitySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207