Comparison of Balance during Dual-Task in-between Cognitively Impaired and Nonimpaired Individuals with Parkinson’s Disease
Bibliographic record
Abstract
Introduction: Parkinson’s disease manifests as bradykinesia, stiffness, tremors, and abnormalities in gait and balance. When performing dual activities, people with cognitive impairments exhibit noticeable alterations in mobility. The purpose of this study was to determine whether balance during dual tasking is related to cognitive deterioration. The aim was to compare balance during dual-task in-between cognitively impaired (CI) and nonimpaired individuals with Parkinson’s disease. The objective was to evaluate balance using timed up and go test (TUG), TUG-manual (TUG-m), and TUG-cognitive (TUG-c) and to compare its scores in both the groups. Materials and Methods: It was a cross-sectional observational study carried out at outpatient department and Parkinson’s societies. The sampling technique was purposive sampling, and the sample size was 22. Subjects were divided into two groups (by stratification method) according to Montreal Cognitive Assessment (MOCA) scores as CI and nonimpaired group. Both groups performed TUG with manual task and cognitive task. The time taken to complete all TUG tests was measured. Results: Comparison of TUG between the groups showed a highly significant difference in TUG and TUG-m tests (P < 0.001) and a significant difference in TUG-c (P = 0.028). Conclusion: The study found a significant difference in balance scores, assessed by the TUG test during dual-task conditions, between CI and nonimpaired individuals with Parkinson’s disease. This highlights the important role cognition plays in balance regulation in Parkinson’s disease.
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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 | 0.000 |
| 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".