PARKINSON’S DISEASE CAN LIMIT THE EFFECTS OF MOTOR-COGNITIVE TRAINING IN VIRTUAL REALITY ENVIRONMENT
Bibliographic record
Abstract
Objective: To evaluate if patients with Parkinson s disease and elderly people can improve on their postural control (PC) and cognition after virtual reality training. Methods: Sample size was composed by ten subjects, 5 idiopathic PD (PDG) [68.2 ± 6.01 years; Hoehn & Yahr scale = 1:2 subject; 1.5:2 subject and 3:1 subject], and 5 elders subjects (ESG) [68.2 ± 6.3 years]. Fourteen sessions of Kinect Adventures games were carried out [1 hour, 2x/week for 7 weeks, during on period of dopaminergic replacement]. PC and cognition were assessed by Mini-Balance Evaluation Systems Test (MBT) and Montreal Cognitive Assessment (MoCA). Assessments were performed before, after and 1 month after the end of training (follow-up). Descriptive analysis was performed (mean, standard deviation and confidence interval of 95%). Results: Regarding PC, MBT scores in PDG were at baseline: 26.0 ± 3.6 [21.52 – 30.47]; after training: 26.2 ± 3.42 [21.95 – 30.44] and follow-up: 27.6 ± 2.6 [24.36 – 30.83]. MBT scores in ESG were at baseline: 27.4 ± 2.7 [24.04 – 30.75], after training: 29.2 ± 2.77 [25.75 – 32.64] and follow-up: 28.4 ± 1.81 [26.14 – 30.65]. MoCA scores in PDG were 24.0 ± 1.87 [21.67 – 26.32] at baseline 23.6 ± 3.2 [19.61 – 27.58] after training and 23.6 ± 2.7 [20.24 – 26.95] at follow up. MoCA scores in ESG were 21.0 ± 3.08 [17.17 – 24.82] at baseline, 27.2 ± 2.16 [24.50 – 29.89] after training and 26.2 ± 2.48 [23.10 – 29.29] at follow up. Conclusion: Scores of groups in both scales at baseline indicated that patients were next to ceiling of scales. Nevertheless, CG showed improvement on PC and cognition after training. However, future studies with larger sample are needed in order to generalize the results.
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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 | 0.000 | 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) | 0.002 | 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".