Effects of Wu Qin Xi exercise on reactive inhibition in Parkinson’s disease: A randomized controlled clinical trial
Bibliographic record
Abstract
Objective: Motor symptom in patients with Parkinson's disease (PD) are related to reduced motor inhibitory ability (proactive and reactive inhibition). Although exercise has been shown to improve this ability, its effects on different levels of motor inhibition have not been determined. Materials and methods: exercise (WQX) and stretching exercise (SE)]. The stop signal task and questionnaires were administered pre and post interventions. Twenty-five age-matched healthy controls were recruited to obtain reference values for inhibition. Results: Compared to healthy controls, patients with PD showed motor inhibition deficits in reactive inhibition, but not in proactive inhibition. Post-intervention, the WQX group showed significant improvement in reactive inhibition compared to the SE group. In both the WQX and SE groups, movement speed was improved post-intervention, accompanied by reduction in negative emotions, stable improvement of sleep quality, and high self-reported satisfaction levels. Conclusion: exercise can improve the reactive inhibition of patients with PD. Our results provide theoretical support for the formulation of reasonable and effective exercise prescriptions for PD rehabilitation. Clinical trial registration: [http://www.chictr.org.cn], identifier [ChiCTR2000038517].
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".