Relationship Between Clinical Measures of Upper Limb Movement Quality and Activity Poststroke
Bibliographic record
Abstract
Background. Understanding the relationship between movement quality (impairment) and performance (activity) in poststroke patients is important for rehabilitation intervention studies. This has led to an interest in kinematic characterization of upper limb motor impairment. Since instrumented motion analysis is not readily clinically available, observational kinematics may be a viable alternative. Objective. To determine if upper limb movement quality during a reach-to-grasp task identified by observation could be used to describe the relationship between motor impairments and the time to perform functional tasks. Methods. Cross-sectional, secondary analysis of baseline data from 141 participants with stroke, age 18 to 85 years, who participated in a multicenter randomized controlled trial. Clinical assessment of movement quality using the Reaching Performance Scale for Stroke (RPSS–Close and Far targets) and of performance (activity) from the Wolf Motor Function Test (WMFT–7 items) was assessed. The degree to which RPSS component scores explained scores on WMFT items was determined by multivariable regression. Results. Clinically significant decreases (>2 seconds) in performance time for some of the more complex WMFT tasks involving prehension were predicted from RPSS–Close and Far target components. Trunk compensatory movements did not predict either increases or decreases in performance time for the WMFT tasks evaluated. Overall, the strength of the regression models was low. Conclusions. In lieu of kinematic analysis, observational clinical movement analysis may be a valid and accessible method to determine relationships between motor impairment, compensations and upper limb function in poststroke patients. Specific relationships are unlikely to generalize to all tasks due to kinematic redundancy and task specificity.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".