Meta-analysis with trial sequential analysis investigating the impact of adjunctive electroacupuncture therapy on vascular mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: To systematically collect, evaluate, and synthesize evidence from randomized controlled trials (RCTs) supporting the use of electroacupuncture (EA) as an additional treatment option for Vascular mild cognitive impairment (VaMCI), a meta-analysis was carried out. METHODS: Electronic searches of eight databases were used to locate RCTs that evaluated EA as a VaMCI adjuvant therapy. The Cochrane Risk of bias was used to assess the included trials' methodological quality. Review Manager 5.4 was used to analyze the data. Trial sequential analysis (TSA) was conducted with the trial sequential analysis program. RESULTS: There were 15 RCTs with 1033 subjects in them. Compared to conventional therapy (CT) alone, the Montreal Cognitive Assessment (SMD 0.72, 95 percent CI [0.55, 0.88]), Mini-mental State Examination (SMD 0.73, 95 percent CI [0.60, 0.87]), and activities of daily living (SMD 0.83, 95 percent CI [0.54, 1.12]) were significantly improved while EA was used in conjunction with CT. The current studies exceeded the required information size, according to trial sequential analysis (TSA), demonstrating the reliability of EA adjuvant therapy VaMCI. CONCLUSIONS: According to the pooled data, EA as an adjunct therapy for the treatment of VaMCI increases clinical efficacy. Although the TSA confirms a stable conclusion, it is encouraged to conduct studies of the highest quality standards.
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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 | 0.071 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.060 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".