Clinical Study on Acupuncture Combined with Chinese Medicine on Patients with Vascular Cognitive Impairment No Dementia
Bibliographic record
Abstract
Objective: Evaluate the efficacy and safety of acupuncture combined with Chinese medicine on patients with vascular cognitive impairment no dementia from cognitive function,activities of daily living and syndromes of traditional Chinese medicine. Method: This is a randomized,active-controlled and open-label trial.In total,eighty patients with vascular cognitive impairment no dementia were enrolled and divided evenly into control and trial groups. Based on the foundation treatment of vascular risk factors,the tail group was given Fuzhi capsule combined with acupuncture,and the control group was given oxiracetam capsules. The mini-mental state examination( MMSE),montreal cognitive assessment( MOCA),activities of daily living( ADL),syndrome differentiation scale of vascular dementia( SDSVD) scores will be observed after a 12-week treatment period.Result: The MMSE and MOCA scores of each group of was increased after the treatment. Compared with the control group,the MMSE and MOCA scores of the trial group increased obviously,the difference was statistically significant( P 0. 05). The ADL and SDSVD scores were reduced after the treatment. Compared with the control group,the ADL and SDSVD scores of the trial group reduced obviously,the difference was statistically significant( P 0.05). Conclusion: Combineing with acupuncture and Chinese medicine has beneficial effects on patients with vascular cognitive impairment no dementia. It can improve the cognitive function and activities of daily living significantly. The results of this study will provide evidence for developing a comprehensive therapy regimen,which can delay the progress of dementia and improve the quality of life for 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".