Evaluation of Knee Proprioception and Factors Related to Parkinson’s Disease
Bibliographic record
Abstract
Background. Changes in proprioception may contribute to postural instability in individuals with neurological disorders. Objectives. Evaluate proprioception in the lower limbs of patients with Parkinson's disease (PD) and the association between proprioception and cognitive ability, motor symptoms, postural instability, and disease severity. Methods. This is a cross-sectional, controlled study that evaluated proprioception in PD patients and healthy age- and sex-matched individuals. Kinetic postural proprioception of the knee was evaluated using an isokinetic dynamometer (Biodex® Multi-Joint System 4 Pro). Participants were evaluated using the Montreal Cognitive Assessment (MoCA), the Hoehn and Yahr rating scale and postural instability (pull test and stabilometric analysis), and motor function (UPDRS-III) tests. Results. A total of 40 individuals were enrolled in the study: 20 PD patients and 20 healthy controls (CG). The PD patients had higher angular errors on the proprioceptive ratings than the CG participants (p = 0.002). Oscillations of the center of pressure (p = 0.002) were higher in individuals with PD than in the controls. Proprioceptive errors in the PD patients were associated with the presence of tremors as the dominant symptom and more impaired motor performance. Conclusion. These findings show that individuals with PD have proprioceptive deficits, which are related to decreased cognitive ability and impaired motor symptoms.
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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 | 0.001 |
| 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) | 0.001 | 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".