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Record W4389478900 · doi:10.1002/mdc3.13952

Arm Swing while Walking and Running: A New Clinical Feature to Separate Parkinson's Disease from Functional Parkinsonism

2023· article· en· W4389478900 on OpenAlexaff
Conor Fearon, Suvorit Subhas Bhowmick, Anouk Tosserams, Daniel G. Di Luca, Jane Liao, Jorik Nonnekes, Bastiaan R. Bloem, Anthony E. Lang

Bibliographic record

VenueMovement Disorders Clinical Practice · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsParkinsonismPhysical medicine and rehabilitationSwingParkinson's diseaseGaitPsychologyMedicinePhysical therapyDiseaseInternal medicinePhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Functional parkinsonism is an important differential diagnosis of Parkinson's disease (PD). Based on anecdotal experience, we hypothesized that arm swing while walking and running could differentiate these two conditions, but this assumption has not been previously explored systematically. OBJECTIVES: To examine differences in arm swing while walking and running between patients with PD and functional parkinsonism. METHODS: We analyzed blinded video assessments of arm swing and other gait parameters in patients with asymmetrical PD (n = 81) and functional parkinsonism (n = 8) while walking and running. The groups were matched for age, sex and disease duration. RESULTS: In contrast to those with PD, patients with functional parkinsonism (i) were more likely to have a marked asymmetry in arm swing while walking (5/8 vs. 25/81; P = 0.06), (ii) were less likely to improve arm swing while running with full effort (3/8 vs. 72/81; P < 0.001) and (iii) demonstrated normal passive arm swing even when asymmetry of arm swing was marked during running/walking (6/6 vs. 9/33; P = 0.002). CONCLUSIONS: Assessment of arm swing while walking and running and passive arm swing could be important differentiating clinical features between functional parkinsonism and 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 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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.089
GPT teacher head0.444
Teacher spread0.355 · 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.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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