Bilateral arm training in the chronic phase of stroke rehabilitation: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Bilateral arm training (BAT) is an intervention utilized in rehabilitating upper-extremity paresis. The objective of this study was to conduct a systematic review and meta-analysis on the evidence for BAT on upperlimb paresis in the chronic phase of stroke. Methods: A literature search of multiple databases (MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, OT Seeker ) was conducted for relevant randomized controlled trials (RCTs) published in the English language that met inclusion criteria. Studies must have included BAT as part of treatment and participants must have been ≥6 months post stroke. Methodological quality of each study was assessed using the PEDro scale (maximum score=10). Results: Eight RCTs satisfied the inclusion criteria (PEDro scores 1-7) for a total pooled sample size of 131 subjects (88 males and 43 females). The mean age of subjects was 57.6±4.1 years (range 50.7-64.8 years) and the mean time since stroke was 43.8±34.8 months (range 13.9-114.0 months). Among study endpoints, only the Fugl-Meyer Assessment tool showed significant improvement in motor impairment whereby the BAT groups improved, on average, 3.77 points whereas the control group improved just 1.23 points (Difference of Means = 1.46±0.662; p=0.028). Conclusion: Overall, BAT showed a general trend in improvement over standard therapy, although it was not statistically significant. Future studies with improved methodological quality (e.g., strict inclusion criteria, protocol standardization) and larger sample sizes are needed to appropriately assess the benefit of BAT in stroke patients.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| 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 | 0.002 | 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".