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Assessment of cognitive impairment based on the clinical heterogeneity of idiopathic Parkinson's disease

2010· article· en· W3031209712 on OpenAlexaboutno aff
刘萍, 冯涛, 张璇, 陈彪

Bibliographic record

Venue中国综合临床 · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitive impairmentCognitionMedicineInternal medicineDepression (economics)Verbal fluency testParkinson's diseaseAlertnessDiseasePsychologyAudiologyPsychiatryNeuropsychology

Abstract

fetched live from OpenAlex

Objective To assess cognitive impairment of patients with idiopathic Parkinson's disease(PD)of different clinical subgroups. Methods The cluster analysis approach was used to classify PD patients on the basis of their clinical features, and then compare their cognitive function according their Montreal Cognitive Assessment (MoCA)score. Results One-hundred and six patients were divided into 5 groups by means of cluster analysis:A. young-onset(n = 35), B. rapid disease progression(n = 6); C. tremor dominant(n = 36), D. haplo-non-tremor dominant(n =18),E. non-tremor dominant with depression(n = 11). The MoCA score and proportion of abnormal scores differed among the 5 groups significantly,especially between the subgroup E(19.00 ±5.47)and the subgroup A(23. 66 ± 3.51)(P 〈 0. 05), the former was more seriously impaired in clock drawing task(1.73 ± 1. 01 vs.2. 66 ±0. 59 ,t = -2. 904,P =0. 013), alertness(0. 55 ±0. 52 vs. 0. 91 ±0. 28 ,t = -2. 241 ,P =0. 045),semantic fluency(0. 64 ±0. 51 vs. 0. 97 ±0. 17 ,t = -3. 429,P =0. 001)and orientation domains than the latter(4. 91 ± 1.38 vs. 5.80±0.47,t = -3.321,P =0.020). Conclusions The cognitive impairment in PD patients are common as well as heterogeneous. Key words: Parkinson's disease;  Cognitive impairment

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)0.001
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 score0.123
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.069
GPT teacher head0.366
Teacher spread0.297 · 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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