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Record W2918398273 · doi:10.1002/brb3.1219

Exploring the association between Cerebral small‐vessel diseases and motor symptoms in Parkinson's disease

2019· article· en· W2918398273 on OpenAlexaboutno aff
Ying Wan, Wenjian Hu, Jing Gan, Lu Song, Na Wu, Yuzhen Chen, Zhenguo Liu

Bibliographic record

VenueBrain and Behavior · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsParkinson's diseaseDiseaseMedicineAssociation (psychology)Physical medicine and rehabilitationMotor symptomsNeuroscienceCardiologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: to explore the association between cerebral small-vessel diseases (CSVDs) and motor symptoms in Parkinson's disease (PD). METHODS: 137 PD patients were recruited into the study. Detailed motor symptoms, including tremor, rigidity, bradykinesia, and axial impairment, were evaluated using Unified Parkinson's disease Rating Scale (UPDRS). Non-motor symptoms, including cognition, anxiety, and depression, were evaluated using Montreal Cognitive Assessment (MoCA), Hamilton anxiety scale (HAMA), and Hamilton depression scale (HAMD). Brain MRI was used to assess the subtypes of CSVDs, including lacunes, enlarged perivascular spaces (EPVS), and white matter hyperintensities (WMH). WMH were furtherly divided into deep WMH (DWMH) and periventricular hyperintensities (PVH). The association between CSVDs and motor symptoms was analyzed. Patients were divided into the postural instability and gait disability (PIGD) group and non-PIGD group. Demographic, clinical and CSVDs variables were compared between the two groups. RESULTS: CSVDs subtypes were all detected in the participants with different prevalence rates and severity degrees. We found a close association between EPVS in basal ganglia and the tremor score (p = 0.032), and between DWMH in the frontal and occipital lobes and the axial motor score (p < 0.05) through the spearman and multivariate liner regression analysis. Compared with the non-PIGD group, the PIGD group demonstrated more serious cognitive impairment and DWMH in the frontal and occipital lobes (p < 0.05). The demographic characteristics and vascular risk factors of the PIGD group were not different from those of the non-PIGD group. Cognitive impairment and DWMH in the frontal lobe were identified to be independent risk factors of PIGD motor phenotype. CONCLUSIONS: We identified a close association between the CSVDs and motor symptoms in PD and DWMH in the frontal lobe was a risk factor of PIGD motor phenotype, which supports the contribution of vascular pathology in PD.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.036
GPT teacher head0.261
Teacher spread0.225 · 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

Citations47
Published2019
Admission routes1
Has abstractyes

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