Effects of Robot-Assisted Therapy for the Upper Limb After Stroke
Bibliographic record
Abstract
BACKGROUND: Robot technology for poststroke rehabilitation is developing rapidly. A number of new randomized controlled trials (RCTs) have investigated the effects of robot-assisted therapy for the paretic upper limb (RT-UL). OBJECTIVE: To systematically review the effects of poststroke RT-UL on measures of motor control of the paretic arm, muscle strength and tone, upper limb capacity, and basic activities of daily living (ADL) in comparison with nonrobotic treatment. METHODS: Relevant RCTs were identified in electronic searches. Meta-analyses were performed for measures of motor control (eg, Fugl-Meyer Assessment of the arm; FMA arm), muscle strength and tone, upper limb capacity, and basic ADL. Subgroup analyses were applied for the number of joints involved, robot type, timing poststroke, and treatment contrast. RESULTS: Forty-four RCTs (N = 1362) were included. No serious adverse events were reported. Meta-analyses of 38 trials (N = 1206) showed significant but small improvements in motor control (~2 points FMA arm) and muscle strength of the paretic arm and a negative effect on muscle tone. No effects were found for upper limb capacity and basic ADL. Shoulder/elbow robotics showed small but significant effects on motor control and muscle strength, while elbow/wrist robotics had small but significant effects on motor control. CONCLUSIONS: RT-UL allows patients to increase the number of repetitions and hence intensity of practice poststroke, and appears to be a safe therapy. Effects on motor control are small and specific to the joints targeted by RT-UL, whereas no generalization is found to improvements in upper limb capacity. The impact of RT-UL started in the first weeks poststroke remains unclear. These limited findings could mainly be related to poor understanding of robot-induced motor learning as well as inadequate designing of RT-UL trials, by not applying an appropriate selection of stroke patients with a potential to recovery at baseline as well as the lack of fixed timing of baseline assessments and using an insufficient treatment contrast early poststroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".