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Record W3171282000 · doi:10.1002/mdc3.13262

Progression in Parkinson's Disease: Variation in Motor and Non‐motor Symptoms Severity and Predictors of Decline in Cognition, Motor Function, Disability, and Health‐Related Quality of Life as Assessed by Two Different Methods

2021· article· en· W3171282000 on OpenAlexaboutno aff
Paulo Bugalho, Filipa Ladeira, Raquel Barbosa, João Pedro Marto, Cláudia Borbinha, Laurete da Conceição, Manuel Salavisa, Marlene Saraiva, Bruna Meira, Marco Fernandes

Bibliographic record

VenueMovement Disorders Clinical Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentQuality of life (healthcare)ApathyCognitionParkinson's diseaseRating scalePsychologyDementiaDiseasePhysical medicine and rehabilitationPhysical therapyPsychiatryMedicineInternal medicineCognitive impairmentDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Parkinson's disease (PD) is multi-symptom disease with variable progression. OBJECTIVES: We performed a longitudinal study to address the evolution of motor symptoms (MS) and non-motor symptoms (NMS), predictors of motor-, cognitive-, disability-, and health-related quality of life (HRQL) status and the relative usefullness of a battery of separate NMS scales (BSS) versus the Non-Motor Symptom Scale (NMSS). METHODS: Seventy-two patients were assessed at baseline and 4 years later with the NMSS and BSS. We assessed the following outcomes: cognition (Montreal Cognitive Assessment scale [MoCA]), disability (Unified Parkinson's Disease Rating Scale Part II [UPDRS II], Schwab and England [S&E]), motor dysfunction (Unified Parkinson's Disease Rating Scale Part III [UPDRS III], Hoehn and Yahr [HY]), and HRQL (EuroQol [EQ] EQ-vertical visual analogue scale [VAS] and EQ-Index). Statistical analysis included a comparison between scales scores at both time points and multivariate regression analysis to calculate the impact of each baseline symptom in outcomes. NMSS and BSS were introduced in separate models. RESULTS: NMSS Domain 4: perception/hallucinations, Parkinson's Psychosis Questionnaire, Apathy Scale, NMSS Domain 7: urinary, S&E, UPDRS II, HY, and MoCA scores worsened significantly. Dementia increased to a 4-year prevalence of 39.8%. In the multivariate model using BSS, cognitive state variation was significantly predicted by baseline HY, EQ-Index, and S&E. Using the NMSS, MoCA change was significantly associated with NMSS Domain 4: perceptions/hallucination score, cognitive status with UPDRS III score, HRQL with NMSS Domain 4: perception/hallucinations score, and S&E. CONCLUSION: Our study suggests that NMS progress heterogeneously, BSS approach being more sensitive to change than NMSS. The multivariate analysis has shown that S&E and NMSS Domain 4: perception/hallucinations scores are the stronger predictors of HRQL and cognitive dysfunction variation, favoring NMSS over the BSS approach.

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)0.002
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.425
Teacher spread0.386 · 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

Citations42
Published2021
Admission routes1
Has abstractyes

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