Treatment of Cognitive Dysfunction after Stroke by Herbs of Reinforcing the Kidney and Promoting Blood Flow Combined with Small Dose of Memantine
Bibliographic record
Abstract
Objective:To observe the effect of herbs of reinforcing the kidney and promoting blood flow combined with low dose of memantine for treatment of cognitive dysfunction after stroke.Method: From January 2009 to June 2011,80 patients with after stroke cognitive dysfunction were collected as investigative cases.And all patients were given the treatment of herbs of reinforcing the kidney and promoting blood flow combined with small dose of memantine.Then we evaluated the curative effect by applying the simple intelligent state examination scale(MMSE) and Montreal cognitive assessment scale(MoCA,Beijing edition) before treatment,a month and three months after treatment.Result: The MMSE score of all patients was 24.84±3.21 and MoCA score was 23.76±3.00 before treatment,those were all below the normal reference value of 26.Along with the treatment continuance,MMSE and MoCA scores were increased gradually,they were 27.28±4.57 and 26.47±3.43 at one month after treatment,and 29.78±4.44 and 28.89±3.37 at three months after treatment.They were higher than those of before treatment(P0.05).Along with treatment continuance,significant efficiency gradually raised.The significant efficiency at three months after treatment was 21.25%,which was higher than that at a month after treatment(15.0%)(P0.05);The total effective rate at three months after treatment was 86.25%,and those at a month after treatment was 83.75%,without significant difference.Conclusion: Treatment by herbs of reinforcing the kidney and promoting blood flow combined with small dose of memantine could improve cognitive function for cognitive dysfunction after stroke.The effect is clear and worthwhile to be applied clinically.
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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".