“Dihuang Yizhi Formula”for the treatment of vascular cognitive impairment no dementia: report of 36 cases
Bibliographic record
Abstract
Objective To observe the clinical efficacy of Dihuang Yizhi Formulafor the treatment of vascular cognitive impairment no dementia( VCIND). Methods Seventy-two cases were randomized into treatment group and control group,with 36 cases in each group. Both of the groups were treated with basic therapeutic measures. Additionally,treatment group was treated withDihuang Yizhi Formula,and control group were treated with donepezil,with the course of 4 months. The changes of MMSE,MOCA,ADL and TCM symptom scores were evaluated. Results In treatment group,there were significant differences in the scores of MMSE,MOCA,ADL and TCM symptom between before and after treatment( P 0. 05); in control group,there were significant differences in the scores of MMSE,MOCA and ADL in control group( P 0. 05). After treatment,significant differences were found in scores of ADL and TCM symptom between the two groups( P 0. 05). Conclusion Dihuang Yizhi Formulacan improve the cognitive function,response capability and clinical symptoms in patients with VCIND of kidney-essence deficiency and phlegm stagnation types.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".