Effect of acupuncture on vascular cognitive impairment (VCI): A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Vascular cognitive impairment (VCI) is a condition associated with cerebrovascular diseases, which causes a heavy burden on both individuals and society. Acupuncture has been used extensively in China to treat these complications. However, the therapeutic efficacy of this treatment remains uncertain. Consequently, we aimed to investigate the clinical effects of acupuncture on VCI. METHODS: Patients (n = 97) were randomly divided into the intervention (n = 48) and the control (n = 49) groups. The intervention group was given donepezil hydrochloride orally once a day for 4 weeks, and the intervention group was combined with acupuncture treatment on the basis of control group once daily, 6 days a week, for a total of 4 weeks. Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment scores were performed before the intervention and after the intervention (4 weeks post-intervention). The levels of interleukin (IL)-1β and IL-6 were measured before the intervention and after the intervention (4 weeks post-intervention). Finally, the clinical effective rate was calculated according to the MMSE scores before and after intervention. RESULTS: Following the intervention, significant differences were observed between the intervention and control groups. After 4 weeks, MMSE and Montreal Cognitive Assessment scores were significantly increased (P < .001), and IL-1β and IL-6 levels were significantly decreased (P < .001). CONCLUSION: Acupuncture treatment can improve the cognitive function of patients with VCI and decrease the levels of IL-1β and IL-6. These findings strongly support the efficacy of acupuncture as a therapeutic intervention in patients with VCI.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".