Do Activity Level Outcome Measures Commonly Used in Neurological Practice Assess Upper-Limb Movement Quality?
Bibliographic record
Abstract
BACKGROUND: Movement is described in terms of task-related end point characteristics in external space and movement quality (joint rotations in body space). Assessment of upper-limb (UL) movement quality can assist therapists in designing effective treatment approaches for retraining lost motor elements and provide more detailed measurements of UL motor improvements over time. OBJECTIVE: To determine the extent to which current activity level outcome measures used in neurological practice assess UL movement quality. METHODS: Outcome measures assessing arm/hand function at the International Classification of Function activity level recommended by neurological clinical practice guidelines were reviewed. Measures assessing the UL as part of a general mobility assessment, those strictly evaluating body function/structure or participation, and paediatric measures were excluded. RESULTS: In all, 15 activity level outcome measures were identified; 9 measures assess how movement is performed by measuring either end point characteristics or movement quality. However, except for the Reaching Performance Scale for Stroke and the Motor Evaluation Scale for Upper Extremity in Stroke Patients, these measures only account for deficits indirectly by giving a partial score if movements are slower or if the person experiences difficulties. Six outcome measures neither assess any parameters related to movement quality, nor distinguish between improvements resulting from motor compensation or recovery of desired movement strategies. CONCLUSION: Current activity measures may not distinguish recovery from compensation and adequately track changes in movement quality over time. Movement quality may be incorporated into clinical assessment using observational kinematics with or without low-cost motion tracking technology.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".