Effect of acupuncture on mild cognitive impairment in the elderly: A randomized controlled trial
Bibliographic record
Abstract
Background and objective: Mild cognitive impairment (MCI) is a cognitive dysfunction syndrome defined mostly by memory or other cognitive impairments, and may serve as a precursor to Alzheimer's disease (AD). In recent years, acupuncture has gained recognition as a potential intervention for MCI, attracting significant attention as a promising and well-established therapy. In this study, we critically evaluate the clinical efficacy and safety of an innovative acupuncture approach, termed "Kidney Nourishment and Spirit Regulation", as a therapeutic modality for MCI in geriatric populations. Methods: A prospective, randomized, single-blind, placebo-controlled, single-center clinical trial design where patients will be allocated in acupuncture, placebo (sham acupuncture sessions), or blank for eight weeks. The blank group will receive health education over the same eight-week period and will be offered compensatory acupuncture therapy after this period. The selected acupoints for this investigation include GV20, EX-HN1, GV24, GV29, CV6, CV4, PC6, KI3, LI4, LR3, HT7 and SP6. The primary outcome measure will be the Montreal Cognitive Assessment (MoCA), while secondary outcomes include the Mini Mental State Examination (MMSE), Activity of Daily Living (ADL), and Electroencephalogram (EEG). Discussion: This study seeks to provide an optimum regimen for acupuncture therapy in elderly MCI patients and to provide considerable theoretical evidence for its popularization and future broad adoption. We thus postulate that the current trial data might enlighten and potentially guide future research in terms of study design refinement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".