Bimanual Coordination in Individuals Post-stroke: Constraints, Rehabilitation Approaches and Measures: Systematic Review
Bibliographic record
Abstract
To couple or not to couple is a dilemma for the CNS when performing bimanual goal-directed actions. Numerous interacting individual and task-related constraints contribute to the issue of effective movement coordination, and their impact on the emerging actions must be inferred from valid methodologies. This is particularly important when examining coordination in individuals with stroke undergoing rehabilitation. The purpose of this review was to identify the different constraints that may impact inter-limb coupling, and the rehabilitation approaches implemented to enhance those actions. Also, the measures incorporated to examine the effects of rehabilitation methods were reviewed. A literature search was conducted using CINAHL, PubMed and PsycINFO. Following the PRISMA 2020 guidelines, 789 relevant studies were identified, with 20 articles fulfilling the established criteria. Results showed that the impact of sex, time after stroke, type of stroke, and age were not examined in any studies reviewed. In terms of task constraints, most did not examine bimanual coordination explicitly. Bimanual movement training was the most prevalent. Regarding the dependent variables, clinician-reported and performance based scales were frequently used, while only eight studies implemented kinematic analysis, and only three examined inter-limb organization. None made explicit inferences to the existing theories of inter-limb coordination. In conclusion, important individual and task constraints on inter-limb coordination were scarcely examined. Also, majority of the studies did not involve bimanual tasks, or any measures of inter-limb coupling, thus the inferences should be treated with caution. Conceptually, all studies were data driven.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".