Therapeutic effect of piracetam with nimodipine on vascular dementia after cerebral infarction.
Bibliographic record
Abstract
This article investigated the clinical effects of piracetam with nimodipine in the treatment of vascular dementia (VD) after cerebral infarction. 98 patients with vascular dementia after cerebral infarction were selected and divided into the control group and the study group according to the treatment method. The control group was treated with nimodipine alone. The study group was treated with piracetam on the basis of this observation, and we test the ADL (life ability score), MoCA(montreal cognitive assessment scale), ADAS-Cog(alzheimer's scale-cognition), MMSE(mental status examination) scores and quality of life scores before and after treatment in the two groups. Before treatment, there were no significant differences in ADL, MoCA, and ADAS-Cog scores between the two groups (P>0.05). After treatment, the ADL, MoCA, and ADAS-Cog scores of the study group were superior to the control group. The difference was statistically significant (P<0.05). There was no significant difference in MMSE scores between the two groups before treatment and 1 month after treatment (P>0.05). The MMSE scores of the study group were better than the control group after 3 months of treatment and half a year after treatment. The difference was statistically significant (P <0.05). Before treatment, there was no significant difference in the quality of life scores between the two groups (P>0.05). After treatment, the quality of life scores was significantly higher than the control group, and the difference was statistically significant (P<0.05). For patients with vascular dementia after cerebral infarction, piracetam combined with nimodipine can improve the cognitive function, improve the quality of life, and have a significant clinical effect.
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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 | 0.001 |
| 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".