Digital interventions for cognitive dysfunction in stroke patients:Systematic Review and Meta-analysis (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> In recent years, digital technologies have shown possibilities for improving cognitive function after stroke, but their effectiveness and treatment options vary, the optimal treatment remains unclear, and the current evidence is somewhat contradictory. </sec> <sec> <title>OBJECTIVE</title> To evaluate the efficacy of various digital interventions in improving post-stroke cognitive function and provide evidence-based support for clinical decision-making. </sec> <sec> <title>METHODS</title> This study adhered to the PRISMA guidelines for systematic review. We conducted searches in PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and CNKI databases from inception to January 2025. Outcomes included the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Randomized controlled trials (RCTs) were included. </sec> <sec> <title>RESULTS</title> A total of 2,128 articles were retrieved, with 27 meeting the inclusion criteria. Compared to conventional rehabilitation or care, computer-assisted cognitive therapy (CACT) demonstrated significant superiority in MoCA scores (MD=2.36, 95% CI=1.45 to 3.27; SUCRA=83.2%); while cognitive training (CCT) demonstrated no statistical difference (MD=0.28, 95% CI=−0.81 to 1.37). For MMSE scores, robot-assisted therapy (RAT) ranked highest in efficacy (MD=5.77, 95% CI=3.47–8.08; SUCRA=99%); whereas both virtual reality (VR) (MD=0.69, 95% CI=−0.93 to 2.31) and CCT (MD=0.78, 95% CI=−1.19 to 2.75) showed no significant improvement. </sec> <sec> <title>CONCLUSIONS</title> Digital therapies effectively improve cognitive function in post-stroke patients. CACT exhibited superior efficacy in MoCA (which emphasizes executive function), while RAT ranked highest in MMSE (which focuses on basic cognition), suggesting distinct domain-specific effects. However, caution is warranted due to heterogeneity, risk of bias, and limited sample sizes in the included studies. Future research should focus on optimizing intervention protocols, integrating neuroregulatory or traditional Chinese rehabilitation techniques, and exploring cost-effective strategies for clinical implementation. </sec> <sec> <title>CLINICALTRIAL</title> CRD420251006601 </sec>
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".