Effect of transcranial direct-current stimulation on cognitive function in stroke patients: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Transcranial direct-current stimulation (tDCS) is a noninvasive approach that can alter brain excitability. Several studies have shown the effectiveness of tDCS in improving language and movement function in stroke patients. However, the effect of tDCS on cognitive function after stroke remains uncertain. METHODS: We searched Medline, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the China National Knowledge Infrastructure, the China Science and Technology Journal Database, and the Wanfang Data Knowledge Service Platform from inception to April 2, 2019. Two reviewers independently screened the studies, extracted the data, and evaluated the quality of the included studies using the Cochrane Collaboration Risk of Bias Tool. All statistical analyses were performed in RevMan 5.3, and the mean difference (MD) or standard mean difference (SMD) were used as the pooled statistics. RESULTS: Fifteen studies involving 820 participants were included. When compared with passive tDCS, anodal tDCS was associated with improved general cognitive performance as examined by the Minimum Mental State Examination or Montreal Cognitive Assessment (SMD = 1.31, 95% CI 0.91-1.71, P < 0.00001), attention performance (SMD = 0.66, 95% CI 0.11-1.20, P = 0.02). There was no significant difference in memory performance (SMD = 0.41, 95% CI -0.67-1.50, P = 0.46). CONCLUSIONS: tDCS is likely to be effective for patients with cognitive impairment after stroke. The evidence for different effects based on population characteristics and stimulation methods was limited, but a real effect cannot be ruled out. More high-quality research in this field is required to determine the potential benefits of tDCS in the treatment of cognitive deficits after stroke and to establish the optimal treatment program.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".