Clincal Observation of Naokang Granula for Treatment of Post-stroke Mild Cognitive Impairment
Bibliographic record
Abstract
Objective To observe the therapeutic effect of Naokang Granula,a Chinese herbal patent with the actions of tonifying liver and kidney,activating blood and removing stasis,resolving phlegm and inducing resuscitation for the treatment of post-stroke mild cognitive impairment with insufficiency of marrow-reservoir.Methods Randomized controlled trial was adopted,and 60 patients with post-stroke cognitive impairment were equally divided into treatment group and control group.The control group received oral administration of Nimodipine tablets while the treatment group was treated with Naokang Granula.Both groups were treated for 6 weeks.A comparison of therapeutic effect was made in both groups before and after treatment by mini-mental state examination(MMSE) and Montreal cognitive assessment scale(MoCA).Results After treatment,the treatment group had a total effective rate of 66.67%,and the control group had a total effective rate of 53.33%.The effective rate in the two groups had no significant difference(P0.05).The scores of MMSE and MoCA were improved in both groups after treatment(P0.01 compared with those before treatment),and the improvement of MoCA in the treatment group was superior to that in the control group(P0.01).Conclusion Naokang Granula is effective on improving cognitive function in patients with post-stroke mild vascular cognitive impairment with insufficiency of marrow-reservoir.
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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 | 0.000 | 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) | 0.002 | 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".