Open-Label Trial Regarding the Use of Acupuncture and <i>Yin Tui Na</i> in Parkinson's Disease Outpatients: A Pilot Study on Efficacy, Tolerability, and Quality of Life
Bibliographic record
Abstract
OBJECTIVES: This study evaluates the effects of sequential tui na massage, acupuncture, and instrument-delivered qigong for patients with Parkinson disease (PD) over a 6-month period. DESIGN: Patients received weekly treatments, which included tui na massage prior to acupuncture followed by instrument-delivered qigong. Each patient was assessed at baseline and at 6 months. SETTING: The setting was an outpatient research/academic clinic for patients with PD and nonacademic acupuncture clinic. SUBJECTS: Twenty-five (25) patients with idiopathic PD were the subjects. OUTCOME MEASURES: Before and after treatment patients were evaluated with the Unified Parkinson Disease Rating Scale (UPDRS), Hoehn and Yahr Staging (H&Y), Schwab and England Activities of Daily Living (S & E), Beck Depression Inventory (BDI), Parkinson's Disease Questionnaire (PDQ-39) quality of life assessment, and patient global assessments. RESULTS: There were no significant improvements in treatment measures; however, there was a 2.4-point worsening in UPDRS motor scores (24.0 versus 26.4, p = 0.018). There was a 16% improvement in the PDQ- 39 total score (23.2 versus 19.6, p = 0.044) and a 29% improvement in the BDI (9.6 versus 6.8, p = 0.006). Sixteen (16) patients reported moderate to marked improvement. There were no adverse effects. CONCLUSIONS: Acupuncture is safe and well tolerated in patients with PD. Most patients reported subjective improvement. The BDI and PDQ-39 total score, measuring depression and quality of life, demonstrated some improvement, but UPDRS motor scores worsened.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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) | 0.005 | 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".