Comparing conventional treatment, single-target rTMS, or dual-target rTMS for the treatment of post-stroke cognitive impairment — clinical effects and neuroscientific insights: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Although increasing evidence suggests that repetitive transcranial magnetic stimulation may help improve cognitive impairment after stroke, its clinical efficacy is still limited. This limitation may be due to the fact that the left dorsolateral prefrontal cortex (DLPFC) is only one of several brain areas involved in post-stroke cognitive impairment (PSCI). The aim of the present study is to reveal whether dual-target stimulation is superior to single-target stimulation and usual care in the treatment of PSCI. METHODS: A single-center, single-blind, randomized controlled trial will be conducted, and fifty-seven PSCI patients will be recruited and randomly assigned to one of three groups based on the stimulating site. The primary outcome is cognitive function, measured using the Montreal Cognitive Assessment Beijing Version (MoCA-BJ) and Mini-Mental Status Examination (MMSE). The secondary outcomes are the modified Barthel Index (MBI), Trail-Making Test (TMT), and digital span test (DST). Furthermore, changes in brain activity are assessed using transcranial Doppler sonography (TCD) examination and serum levels of brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) closely related to nerve and vascular repair after brain injury. All outcomes will be measured at baseline and 4 weeks after treatment. DISCUSSION: If dual-target rTMS in significant improvements in cognitive function, this method could be considered as a first-line clinical treatment for PSCI. This proposed study has the potential to identify a new, evidence-based intervention that can enhance cognition and independent living in patients with cognitive impairment after stroke. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200066184 . It was registered on 26 November 2022.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".