Impact of repetitive transcranial magnetic stimulation on cognitive impairment in stroke patients: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of repetitive transcranial magnetic stimulation (rTMS) on cognitive impairment in stroke patients using meta-analysis. METHODS: Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched up to October 20, 2024. Randomized controlled trials assessing the effect of rTMS on cognitive impairment in stroke patients were included. Pooled effect sizes were calculated using standardized mean differences (SMD) or mean differences (MD) based on fixed or random effects models. RESULTS: Fourteen studies involving 597 stroke patients were included in this meta-analysis. The results showed that rTMS significantly improved global cognitive function in stroke patients (SMD: 0.54; 95 % CI: 0.28, 0.79), particularly with high-intensity rTMS (SMD: 0.41; 95 % CI: 0.07, 0.76) and intermittent theta-burst stimulation (iTBS) (SMD: 0.94; 95 % CI: 0.62, 1.27). In terms of different rating scales, rTMS significantly increased scores on the Modified Barthel Index (MBI) (MD: 10.54; 95 % CI: 4.85, 16.23) and the Montreal Cognitive Assessment (MoCA) (MD: 1.96; 95 % CI: 0.91, 3.01), while the effects on the Mini-Mental State Examination (MMSE), Rivermead Behavioral Memory Test (RBMT), and Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) were not statistically significant. CONCLUSION: This meta-analysis confirms that rTMS can effectively improve cognitive function in stroke patients. However, due to the limited number of studies included, further high-quality research is needed to substantiate our findings.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.060 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".