High-Frequency Repetitive Transcranial Magnetic Stimulation on Overall Cognition in Patients With Poststroke Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to comprehensively assess the recovery effects of high-frequency repetitive transcranial magnetic stimulation in patients with poststroke cognitive impairment. METHODS: Six English and four Chinese databases were searched for relevant studies published up to January 2022. Randomized controlled trials of patients with poststroke cognitive impairment treated with high-frequency repetitive transcranial magnetic stimulation were included. Included studies were assessed for the risk of bias through the Cochrane Intervention Systematic Review Manual 5.1.0. The meta-analysis was performed using RevMan 5.4 software. The PRISMA 2020 guidelines were followed. RESULTS: Sixty-one randomized controlled trials (4012 patients) were included. Montreal Cognitive Assessment Score, Mini-Mental State Examination score, event-related potential P300 (P300) amplitude, Loewenstein Occupational Therapy Cognitive Assessment score, and total effective rate of cognitive function were higher in the high-frequency repetitive transcranial magnetic stimulation group than in the control group at the end of the treatment period, and scores of Alzheimer Disease Assessment Scale-Cognitive Subscale and P300 latency were lower in the high-frequency repetitive transcranial magnetic stimulation group than in the control group. Fifty studies had selected the dorsolateral prefrontal cortex as the stimulation site for high-frequency repetitive transcranial magnetic stimulation. CONCLUSIONS: Compared with nonrepetitive transcranial magnetic stimulation or sham repetitive transcranial magnetic stimulation, high-frequency repetitive transcranial magnetic stimulation not only improves the overall cognitive function of poststroke cognitive impairment patients but also has better rehabilitation results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".