The Therapeutic Effect of Buyang Huanwu Decoction on Mild Cognitive Impairment (MCI) in Patients with Diabetes
Bibliographic record
Abstract
Objective This study aims to comprehensively verify the efficacy of Buyang Huanwu Decoction in improving cognitive function in patients with diabetes.Methods Patients clinically diagnosed with mild cognitive impairment (MCI) assigned to either the placebo group or the Buyang Huanwu Decoction group. After strict screening and exclusions, a total of 156 participants completed the clinical trial, with 76 in the placebo group and 80 in the Buyang Huanwu Decoction group.Results After treatment, Buyang Huanwu Decoction group showed higher Mini-Mental State Examination and Montreal Cognitive Assessment scores compared to placebo (p < 0.05). Memory and Executive Screening, Boston Naming Test, and Animal Fluency Test scores were also higher in the treatment group (p < 0.05). No significant differences were found in DST and CDT scores (p > 0.05). Trail Making Test scores were lower in the treatment group (p < 0.05). No significant difference was observed between the two groups in terms of complications (p > 0.05).Conclusion Patients receiving Buyang Huanwu Decoction treatment demonstrated improvement in cognitive function, showing positive effects and providing preliminary evidence for the role of Buyang Huanwu Decoction in improving cognitive function in patients with diabetes. This suggests its potential for clinical application and further promotion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".