Efficacy of repetitive transcranial magnetic stimulation on post‐stroke cognitive impairment: A systematic and a network meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the efficacy of different repetitive transcranial magnetic stimulation (rTMS) modes in stroke patients with cognitive impairment, and to rank the best option according to the outcome measures. METHODS: Literature was searched in PubMed, Cochrane Library, Web of Science, Embase, SinoMed, China National Knowledge Infrastructure, Wanfang Database, and VIP Database, from database inception to September 2023. We included randomized controlled trials (RCTs) investigating the efficacy of all rTMS modes for post-stroke cognitive impairment. The selected studies assessed at least one of the following outcome measures: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), P300 latency and amplitude, and modified Barthel Index (MBI) or BI. Two researchers independently conducted data extraction. Quality assessment was performed using RevMan 5.3 software based on the Cochrane Collaboration's tool, and statistical analysis was conducted by GeMTC 0.14.3 software and Stata 17.0 software. RESULTS: The network meta-analysis included 74 RCTs with a total of 5478 patients. The best probability ranking indicated that intermittent theta burst stimulation (iTBS) was the most effective in enhancing MoCA, MMSE and MBI scores (85%, 54%, 42%, respectively), followed by 10 Hz rTMS (79%, 50%, 39%, respectively), for P300 amplitude, ≤1 Hz rTMS was ranked first (52%). CONCLUSIONS: The current limited evidence suggests that iTBS may be the optimal approach for improving cognitive and daily life abilities of stroke patients, followed by 10 Hz rTMS, ≤1 Hz rTMS may be the preferred option for enhancing P300 amplitude. TRAIL REGISTRATION: PROSPERO 2023 CRD42023424771 available from: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=424771.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".