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Record W2294838332 · doi:10.1159/000444021

Age, Gender, Comorbidity, and the MDS-UPDRS: Results from a Population-Based Study

2016· article· en· W2294838332 on OpenAlexafffund
Mark R. Keezer, Christina Wolfson, Ronald B. Postuma

Bibliographic record

VenueNeuroepidemiology · 2016
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsMontreal General Hospital
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsMedicineComorbidityPopulationGerontologyEpidemiologyDemographyPsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding sources of variation in International Parkinson and Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores is essential for planning clinical trials in Parkinson's disease and interpreting studies of mild parkinsonian signs. METHODS: We describe the characteristics of the MDS-UPDRS in a population-based sample of individuals without parkinsonism. Multiple linear regression and Spearman's rank correlation coefficients were used to examine potential associations. RESULTS: Among 194 consecutive individuals without parkinsonism, the mean total MDS-UPDRS score was 12.5 (SD 9.8). Sixty-nine percent (134/193) had motor examination (Part III) scores of 2 or more, 16% (30/194) had scores of 10 or more. Female sex, arthritis or spondylosis, diabetes mellitus, and essential tremor were found to be associated with statistically significant increases in MDS-UPDRS Part III scores. For every 10-year increase in age, the Part III score was greater on average by 2.2 (1.5-2.8). CONCLUSIONS: Elevated MDS-UPDRS scores are common in the general population. The overall burden of motor signs of parkinsonism is especially high in older age groups, in women, and in those with particular comorbidities. Whether this represents evidence of a subclinical neurodegenerative process or the effect of comorbid conditions requires further examination.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.080
GPT teacher head0.334
Teacher spread0.254 · 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

Citations41
Published2016
Admission routes2
Has abstractyes

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