Efficacy of acupuncture and pharmacological therapies for vascular cognitive impairment with no dementia: a network meta-analysis
Bibliographic record
Abstract
Background and objective Vascular cognitive impairment with no dementia (VCIND) is considered to be the prodromal stage of vascular dementia, characterized by insidious onset. Although acupuncture and drug therapies are effective, the optimal therapy for VCIND remains to be further determined. Therefore, we conducted a network meta-analysis to compare the effectiveness of acupuncture therapies and current common medicines for VCIND. Methods We searched eight electronic databases to identify eligible randomized controlled trials of patients with VCIND treated by acupuncture or drug therapies. The primary outcome was Montreal Cognitive Assessment, and the secondary outcome was Mini-Mental State Examination. We conducted the network meta-analysis within a Bayesian framework. Weighted mean difference with 95% confidence intervals were applied as effect sizes to continuous data for all outcomes. Sensitivity analysis was done to assess the robustness of the findings, and we also carried out a subgroup analysis based on age. We assessed the risk of bias using the Risk of Bias 2.0 tool and applied the Grade of Recommendation Assessment, Development and Evaluation (GRADE) to assess the quality of the outcomes. This study was registered with PROSPERO, number CRD42022331718. Results A total of 33 studies with 14 interventions were included, including 2603 participants. In terms of the primary outcome, manual acupuncture plus herbal decoction was considered to be the most effective intervention (P = 91.41%), followed by electroacupuncture (P = 60.77%) and manual acupuncture plus piracetam (P = 42.58%), whereas donepezil hydrochloride ranked the least efficacious intervention (P = 54.19%). For the secondary outcome, electroacupuncture plus nimodipine was considered to be the most effective intervention (P = 42.70%), followed by manual acupuncture plus nimodipine (P = 30.62%) and manual acupuncture (P = 28.89%), whereas nimodipine ranked the least efficacious intervention (P = 44.56%). Conclusion Manual acupuncture plus herbal decoction might be the most effective intervention for VCIND. The combination of acupuncture and drug therapy had a tendency to perform better than monotherapy in terms of clinical outcomes. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=331718 , identifier: CRD42022331718.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.050 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".