Yoga as an Add-on Therapy in Parkinson’s Disease: A Single Group Open-label Trial
Bibliographic record
Abstract
ABSTRACT Objective: We aimed to evaluate the effect of yoga on motor and non-motor symptoms and cortical excitability in patients with Parkinson’s disease (PD). Methods: We prospectively evaluated 17 patients with PD at baseline, after one month of conventional care, and after one month of supervised yoga sessions. The motor and non-motor symptoms were evaluated using the Unified Parkinson’s disease Rating Scale (motor part III), Hoehn and Yahr stage, Montreal Cognitive Assessment, Hamilton depression rating scale, Hamilton anxiety rating scale, non-motor symptoms questionnaire and World Health Organization quality of life questionnaire. Transcranial magnetic stimulation was used to record resting motor threshold, central motor conduction time, ipsilateral silent period (iSP), contralateral silent period (cSP), short interval intracortical inhibition (SICI), and intracortical facilitation. Results: The mean age of the patients was 55.5 ± 10.8 years, with a mean duration of illness of 4.0 ± 2.5 years. The postural stability of the patients significantly improved following yoga (0.59 ± 0.5 to 0.18 ± 0.4, p = 0.039). There was a significant reduction in the cSP from baseline (138.07 ± 27.5 ms) to 4 weeks of yoga therapy (116.94 ± 18.2 ms, p = 0.004). In addition, a significant reduction in SICI was observed after four weeks of yoga therapy (0.22 ± 0.10) to (0.46 ± 0.23), p = 0.004). Conclusion: Yoga intervention can significantly improve postural stability in patients with PD. A significant reduction of cSP and SICI suggests a reduction in GABAergic neurotransmission following yoga therapy that may underlie the improvement observed in postural stability. Clinicaltrialsgov identifier: CTRI/2019/02/017564
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".