Efficacy of acupuncture for Parkinson’s disease with mild cognitive impairment: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Parkinson's disease with mild cognitive impairment (PD-MCI) is one of the primary non-motor symptoms of Parkinson's disease (PD). PD-MCI represents an early stage of cognitive impairment in PD and serves as a potential precursor to PDD. To date, research on the treatment of mild cognitive impairment in PD remains limited. Acupuncture, a classical therapeutic modality in Traditional Chinese Medicine, exhibits superior therapeutic outcomes to pharmacotherapy for mild cognitive impairment while avoiding drug-associated adverse effects. Objective: This randomized, single-blind clinical trial aims to evaluate the efficacy and safety of acupuncture for PD-MCI. To improve the credibility of acupuncture research evidence through employing a sham acupuncture device as a control. Methods and analysis: This is a prospective, sham-controlled, subject-blinded and assessor-blinded trial, which conducted at a single center in China. A total of 72 eligible PD-MCI volunteers will be randomized using a simple randomization method in a 1:1 ratio into the acupuncture group and the placebo acupuncture group to receive either acupuncture or placebo acupuncture for 20 sessions over a succession of 5 weeks. The primary outcome measure will be the Montreal Cognitive Assessment Scale (MoCA) score. The secondary outcome measures will be the scores of Mini-Mental State Examination (MMSE), Unified Parkinson's Disease Rating Scale Motor Examination (UPDRSIII) and the level of neurofilament light polypeptide (NfL) and glial cell-derived neurotrophic factor (GDNF). The evaluation will be assessed before and after treatment. Discussion: This study represents the first randomized, single-blind clinical trial investigating acupuncture treatment for cognitive dysfunction in Parkinson's disease. An auxiliary device designed by our team, featuring a flat-head needle and adjustable sleeve, will be used for placebo acupuncture procedure to achieve a single-blind effect. Serum NfL and GDNF levels will be incorporated to elucidate the mechanisms underlying acupuncture's effects and explore specific biomarkers of PD. The aim of this study is to provide reliable clinical evidence for the treatment of PD-MCI and improve patients' survival and quality of life. Clinical trial registration: https://www.chictr.org.cn/, identifier ChiCTR2400082082.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".