Enhanced Parkinson’s gait, reduced fall risk, and improved cognitive function through multimodal rehabilitation combined with rivastigmine treatment
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the effects of combined rehabilitation and rivastigmine treatment on patients with Parkinson's disease (PD). METHODS: Gait parameters were assessed using the Gibbon Gait Analyzer in fifteen patients. Baseline gait data and cognitive assessments were collected. Each patient underwent external counterpulsation therapy, transcranial magnetic stimulation therapy, and exercise therapy for one hour per day, five days a week for three weeks. Post-intervention, gait and cognitive data were re-evaluated. Alongside their standard PD medications, all participants were administered rivastigmine throughout the study period. RESULTS: The intervention significantly enhanced motor function in the single-task test, evidenced by marked improvements in gait metrics such as stride width and walking speed, and a substantial reduction in fall risk. Cognitive function, assessed by mini-mental state examination and Montreal cognitive assessment, showed an improvement trend after the three-week intervention. Improvements in dual-task walking function were observed, although these changes did not reach statistical significance. CONCLUSION: Multimodal exercise training combined with rivastigmine treatment significantly improves certain gait parameters in the single-task test, enhances balance, and reduces the risk of falling in patients with PD. Cognitive function also demonstrated improvement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".