Effects of Acupuncture on Vascular Cognitive Impairment with No Dementia: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Acupuncture has been used for treating vascular cognitive impairment, but evidence for its effectiveness remains limited. OBJECTIVE: This single-center, patient-accessor blinded, randomized controlled trial was designed to assess whether acupuncture could improve the cognitive function of patients with vascular cognitive impairment with no dementia (VCIND). METHODS: 120 VCIND patients were randomly assigned to the electro-acupuncture (EA) or sham acupuncture (SA) group at a 1 : 1 ratio, with treatment conducted thrice weekly for 8 weeks. The primary outcome was the changes of cognitive function measured by the Montreal Cognitive Assessment (MoCA) from baseline to week 8. The secondary outcomes included the scores of the Mini-Mental State Examination (MMSE), the Modified Barthel Index (MBI) and the Self-rating Depression Scale (SDS). Follow-up assessments were performed with MoCA and MMSE at week 16 and 32. Linear mixed-effects models were used for analysis and all statistical tests were two-sided. RESULTS: The results showed that patients in the EA group had a significantly greater improvement in MoCA score (23.85±4.18) than those in the SA group (21.48±4.44) at week 8 (95% CI = 0.80, 3.92, p = 0.04), as well as higher MoCA scores over time (p < 0.001 for interaction). Patients who received EA showed a greater increase in MMSE scores (26.41±3.47) than those who received SA (24.40±3.85) along 8 weeks (95% CI = 0.69, 3.34, p = 0.004). However, results diminished over time. No serious adverse events occurred during the trial. CONCLUSION: EA is a safe and effective technique to improve cognition over the short term of 8 weeks in VCIND patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".