Effect of Transcranial Magnetic Stimulation in combination with nimodipine on vascular dementia after cerebral infarction
Bibliographic record
Abstract
Objective To investigate the effect of Transcranial Magnetic Stimulation(TMS) in combination with nimodipine on vascular dementia after cerebral infarction.Methods 114 patients with vascular dementia after cerebral infarction were randomly assigned to the control group(n=35),nimodipine group(n=40) and nimodipine combined TMS group(n=39).Each patient was given conventional drugs therapy.The control group was only given conventional drugs.Nimodipine group was added nimodipine tablets(30mg,qd,four weeks).TMS group was added low frequency electromagenetic therapy.Nimodipine in combination with TMS therapy group received TMS treatment for 30 minutes(two times a day,for 28 days).The three groups were assessed with mini-mental state examination(MMSE),Montreal Cognitive Assessment Scale(MoCA) and Hasegawa Dementia scale(HDS-R) before and after treatment.Results After four weeks,MMSE scores,MoCA scores and HDS-R scores of nimodipine combined with TMS therapy increased compared with the other two groups(P0.05).The total effective rate was 87.18%,and curative powers was superior to using nimodipine alone or conventional drugs therapy(P0.05).Conclusion Comprehensive treatment of nimodipine in combination with TMS therapy could improve clinical symptoms of patients with VD.
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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".