The effects of computer‐assisted cognitive rehabilitation on cognitive impairment after stroke: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: To determine the effectiveness of computer-assisted cognitive rehabilitation in improving cognitive function in patients with post-stroke cognitive impairment. BACKGROUND: In recent years, computer-assisted cognitive rehabilitation has been accepted as a good substitute or supplement for traditional cognitive rehabilitation. Some clinical randomised controlled trials have been carried out, but no relevant systematic evaluations have been performed. Therefore, we conducted a systematic review of studies involving computer-assisted cognitive rehabilitation to provide evidence-based data for its promotion and application. METHODS: Nine databases (Cochrane Library, PubMed, Web of Science, Embase, OVID, Wanfang Data, CNKI, VIP and SinoMed databases) were systematically searched. Randomised controlled trials that assessed computer-assisted cognitive rehabilitation for patients with post-stroke cognitive impairment were included. Two reviewers appraised the risks of bias through the Cochrane Collaboration's tool and performed the meta-analysis, including the assessment of heterogeneity. We follow the PRISMA 2020 guidelines. RESULTS: Thirty-two studies comprising 1837 participants were included. Compared with conventional therapy alone, the addition of computer-assisted cognitive rehabilitation significantly improved the global cognition of patients, evaluated using the Montreal cognitive assessment, mini-mental state examination and Loewenstein occupational therapy cognitive assessment (p < .01 for all tests). The therapy also significantly improved activities of daily living, assessed using the Barthel index, modified Barthel index and functional independence measure (p < .05 for all tests). CONCLUSION: Computer-assisted cognitive rehabilitation significantly improved the cognitive function and activities of daily living of patients with post-stroke cognitive impairment. RELEVANCE TO CLINICAL PRACTICE: Computer-assisted cognitive rehabilitation can be a valuable technique for cognitive rehabilitation after stroke. It is advantageous for improving patient cognition and restoring the overall functional state of patients. Moreover, the research findings can provide suggestions and inspiration for researchers to implement the proposal, which is conducive to the design of more rigorous and high-quality randomised controlled trials.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| 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".