Repeated transcranial magnetic stimulation combined with Donepezil can improve the cognition of cognitively impaired stroke survivors
Bibliographic record
Abstract
Objective To investigate the effect of repetitive transcranial magnetic stimulation (rTMS) combined with donepezil on the cognition of persons with post-stroke cognitive impairment (PSCI) and their ability to perform activities of daily living (ADL). Methods A total of 106 PSCI patients were randomly divided into an observation group and a control group using a random number table. Those in the observation group received 10Hz rTMS (5 seconds on and 25 seconds off for 20 minutes daily) and donepezil daily, 5 days per week for 4 weeks, while those in the control group were provided with donepezil but only sham rTMS on the same schedule. Before and after 4 weeks of treatment, the Montreal cognitive assessment scale (MoCA), the Rivermead behavior memory test (RBMT) and the modified Barthel index (MBI) were used to evaluate the subjects′ cognitive functioning, memory capacity and ADL ability. The latency and amplitude of auditory event-related potential P300 were also assessed using a myoelectric evoked potential apparatus. Results After the treatment, improvement was observed in all the measurements of both groups. After the treatment, the average MoCA, RBMT and MBI scores, as well as the latency and amplitude of P300 in the observation group were all significantly better than among the control group. Conclusions rTMS can supplement donepezil′s ability to improve the cognition and ADL ability of persons with PSCI. Such therapy is worthy of clinical promotion and application. Key words: Stroke; Cognitive impairment; Transcranial magnetic stimulation; Donepezil
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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".