Arm Swing while Walking and Running: A New Clinical Feature to Separate Parkinson's Disease from Functional Parkinsonism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".