Upper Limb Coordination in Individuals With Stroke: Poorly Defined and Poorly Quantified
Bibliographic record
Abstract
BACKGROUND: The identification of deficits in interjoint coordination is important in order to better focus upper limb rehabilitative treatment after stroke. The majority of standardized clinical measures characterize endpoint performance, such as accuracy, speed, and smoothness, based on the assumption that endpoint performance reflects interjoint coordination, without measuring the underlying temporal and spatial sequences of joint recruitment directly. However, this assumption is questioned since improvements of endpoint performance can be achieved through different degrees of restitution or compensation of upper limb motor impairments based on the available kinematic redundancy of the system. Confusion about adequate measurement may stem from a lack a definition of interjoint coordination during reaching. METHODS AND RESULTS: We suggest an operational definition of interjoint coordination during reaching as a goal-oriented process in which joint degrees of freedom are organized in both spatial and temporal domains such that the endpoint reaches a desired location in a context-dependent manner. CONCLUSIONS: In this point-of-view article, we consider how current approaches to laboratory and clinical measures of coordination comply with our definition. We propose future study directions and specific research strategies to develop clinical measures of interjoint coordination with better construct and content validity than those currently in use.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".