RECOMMENDATIONS FOR BRADYKINESIA ASSESSMENT IN PARKINSON DISEASE
Bibliographic record
Abstract
Purpose/Hypothesis: The purpose of this study was to examine whether active flexion and extension at the elbow joint is an effective method of quantifying bradykinesia in PD (Parkinson disease). Bradykinesia is a cardinal symptom of PD that is conventionally assessed at the digits via finger tapping movements. However, when quantifying bradykinesia with finger tapping, it is often confounded by entrainment of tremor. Terefore, we sought to determine whether rapid alternating movement at the elbow joint would provide a more effective measure of bradykinesia in people with PD. We also examined whether bradykinesia measurements were related to upper extremity rigidity measured at the elbow joint. Number of Subjects: Eight subjects with PD were tested on their more involved side and 4 healthy control subjects were tested on their dominant side. Subjects with PD were of medication. Materials/Methods: Subjects were seated comfortably in a chair. To quantify bradykinesia, subjects were instructed to 1) tap their fngers as fast as they could and 2) move through their full elbow range of motion as big and as fast as they could. We recorded total excursion as well as velocity of movements for three ffteen-second trials using a 3-D motion capture system (Motion Analysis Corporation, Santa Rosa, CA). To assess rigidity, the subjects were told to relax their arm as best as they could while the tester passively moved their arm into full flexion and extension. Total impedance was measured with a Rigidity Analyzer (Neurokinetics, Alberta, Canada) and averaged over three fifty-second trials. T-tests were used to compare bradykinesia measurements between groups and a Pearson product moment correlation was performed within the PD group to examine the relationship between bradykinesia and rigidity measures. Results: There was no difference in finger tapping velocity between the groups. There was a significant group difference for elbow velocity between the PD and control groups (p = 0.029). There was also a strong negative correlation (r = −0.805) between elbow joint excursion and rigidity in the PD group. Conclusions: We have demonstrated that repeated active elbow flexion and extension is an effective method of assessing bradykinesia in PD and can reveal deficits that may not be detected using a finger tapping task. We also speculate that rigidity is a contributing factor in hindering movements of the arm in patients with PD. Clinical Relevance: Assessment of bradykinesia may be done proximally at the elbow joint, rather than at the fngers, to eliminate the influence of distal tremor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".