Association Between Turn Impairments and Cognitive Function in Parkinson Disease
Bibliographic record
Abstract
Objective: To investigate the association of turn parameters with cognitive status in patients with Parkinson disease (PD) and determine the value of turn performance in distinguishing PD-related cognitive impairment (CI) from normal cognition (NC). Methods: This study recruited 168 patients with PD, including 102 patients with NC and 66 patients with CI. The participants performed 180° turn performance trials during the Timed Up and Go walk and 360° turn trials in place using the MATRIX wearable system. Four turn parameters, namely, turn duration, step count, mean turn angular velocity (MAV), and peak turn angular velocity (PAV), were evaluated. Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were performed to evaluate cognitive function. Results: In comparison with the PD-NC group, the PD-CI group showed significantly higher turn duration and step counts and lower MAV and PAV during both 180° and 360° turns. The four turn parameters were significantly correlated with MMSE and MoCA scores after correction for age and educational level. Regression models suggested that the risk of PD-CI was associated with step counts and MAV during 360° turns. The area under the curve values of the step counts and MAV during 360° turns for distinguishing PD-CI from PD-NC were 0.781 and 0.789, respectively. Conclusion: Our findings indicate that turn performance is associated with cognitive status in patients with PD. Assessment of 360° turn characteristics during routine clinic visits would provide a better understanding of CI status in individuals with 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 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 | 0.001 |
| 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".