The Effect of Robot-Assisted Training on Arm Function, Walking, Balance, and Activities of Daily Living After Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This meta-analysis aimed to compare the effects of robot-assisted training (RAT) with those of conventional therapy (CT), considering the potential sources of heterogeneity in the previous studies. We searched three international electronic databases (MEDLINE, Embase, and the Cochrane Library) to identify relevant studies. Risk of bias assessment was performed using the Cochrane's Risk of Bias 1.0 tool. The certainty of the evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations method. The meta-analyses for each outcome of the respective domains were performed using 24 randomized controlled trials (RCTs) on robot-assisted arm training (RAAT) for arm function, 7 RCTs on RAAT for activities of daily living (ADL), 12 RCTs on robot-assisted gait training (RAGT) for balance, 6 RCTs on RAGT for walking, and 7 RCTs on RAGT for ADL. The random-effects model for the meta-analysis revealed that RAAT has significant superiority over CT in improving arm function, and ADL. We also showed that RAGT has significant superiority over CT in improving balance. Our study provides high-level evidence for the superiority of RAT over CT in terms of functional recovery after stroke. Therefore, physicians should consider RAT as a therapeutic option for facilitating functional recovery after stroke.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".