020 Postural sway as a measure of disease severity in parkinson’s disease
Bibliographic record
Abstract
Introduction The severity of Parkinson’s disease (PD) is difficult to assess accurately owing to the lack of a robust biological marker of disease progression, with consequent implications for prognosis and treatment. The current standard measure is the Unified Parkinson’s Disease Rating Scale (UPDRS) but this is hampered by considerable variability between observers and within subjects. Postural sway correlates well with complex brain functioning in other conditions. This study aimed to investigate the correlation of postural sway with the UPDRS and other non-motor measures of disease severity in patients with PD. Methods 28 patients with PD (mean age 68 years, range 54–91; 18 male) underwent tests of cognition and quality of life [Montreal Cognitive Assessment (MoCA), Neuropsychiatry Unit Cognitive Assessment (NUCOG) and Parkinson’s Diseases Questionnaire (PDQ-39–1)], assessment of postural sway using a force plate, and assessment of clinical status using the motor component of the UPDRS. Results Sway path length showed strong correlations with PDQ-39–1, MoCA and the verbal fluency component of the NUCOG (r=0.63,–0.75 and −0.57, respectively; p=0.002,<0.001 and 0.002, respectively) and, to a lesser degree, with the UPDRS III (r=0.45, p=0.018). Conclusion Postural sway shows potential as a sensitive measure of disease severity and brain function in PD, either alone or in combination with other measures. It appears to correlate better with measures of cognition, both general and executive (verbal fluency), and the PDQ measure of disease severity than with the motor component of the UPDRS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".