Evaluating Electroacupuncture Assessment Strategies for Mild Cognitive Impairment: A Scoping Review
Bibliographic record
Abstract
Objective: This scoping review aimed to identify and analyze the range of assessment tools used in electroacupuncture trials for mild cognitive impairment (MCI) and examine emerging trends in study design and evaluation.Methods: In May 2025, seven electronic databases, the International Clinical Trials Registry Platform, and ClinicalTrials.gov were searched for clinical reports and protocols on electroacupuncture for MCI. Studies were screened for participant characteristics, intervention protocols, comparators, and outcome measures.Results: Seventeen reports and three protocols were reviewed. The Mini-Mental State Examination and Montreal Cognitive Assessment were the most frequently used tools. Recent studies incorporated broader neuropsychological batteries, daily functioning, quality of life, emotional state, and brain activity assessments. Electroacupuncture most often targeted GV20, EX-HN1, and GV24, with 30-min sessions across 24 treatments. Controls included pharmacological treatments, sham electroacupuncture, and cognitive rehabilitation.Conclusions: Electroacupuncture appears to be a promising intervention for mild cognitive impairment, particularly the amnestic subtype. However, considerable heterogeneity in outcome measures and protocol design underscores the need for standardized core outcome sets that incorporate cognitive, functional, emotional, and biological domains. Future research should also adopt rigorous sham controls, develop standardized electroacupuncture treatment protocols, ensure transparent safety reporting, and explore the neurophysiological mechanisms underlying electroacupuncture effects.
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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 | 0.028 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".