Electroacupuncture therapy on non-motor symptoms of patients with ParkinsonÓ?s disease: results of a pilot study
Bibliographic record
Abstract
Objective: This study aims to assess the effect of electroacupuncture (EA) on non-motor symptoms in Parkinson's disease (PD) patients as a primary goal and motor symptomatology as a secondary outcome.Methods: Twenty-five patients were enrolled in a non-controlled pilot study that involved a 10-session EA intervention in 16 acupoints, applied 3 times a week for 4 weeks.Motor, non-motor, cognitive, and quality of life evaluation were conducted before intervention and 7 days after concluding the last EA session through MDS-Unified PD rating scale (MDS-UPDRS), Non-motor Symptom Scale (NMSS), montreal cognitive assessment (MoCA), and PD questionnaire (PDQ-8), respectively.Results: Patients showed significantly lower scores in the MDS-UPDRS Part II (7.0 ± 5.7 vs. 10.5 ± 7.6, p = 0.046) and Part III (14.0 ± 8.6 vs. 23.1 ± 13.9, p = 0.002), and NMSS total score (35.2 ± 26.6 vs. 54.6 ± 32.5, p = 0.004) in the post-intervention evaluation, with mood/cognition domain of the NMSS being the only significantly affected by treatment.MoCA total score increased after the intervention (24.2 ± 4.5 vs. 21.6 ± 4.3, p = 0.020), while PDQ-8 scores were not significantly affected by the intervention.Conclusions: Non-motor and motor symptomatology were significantly improved after concluding a 10-session EA therapy.Mood and cognitive disorders were the most positively affected by the intervention.Evaluation of the long-term effects of EA in PD is further needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".