Validity of Movement Pattern Kinematics as Measures of Arm Motor Impairment Poststroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Upper limb motor impairment poststroke is commonly evaluated using clinical outcome measures such as the Fugl-Meyer Assessment. However, most clinical measures provide little information about motor patterns and compensations (eg, trunk displacement) used for task performance. Such information is obtained using movement quality kinematic variables (joint ranges, trunk displacement). Evaluation of movement quality may also help distinguish between levels of motor impairment severity in individuals poststroke. Our objective was to estimate concurrent and discriminant validity of movement quality kinematic variables for pointing and reach-to-grasp tasks. METHODS: A retrospective study of kinematic data (sagittal trunk displacement, shoulder flexion, shoulder horizontal adduction, elbow extension) and Fugl-Meyer Assessment scores from 86 subjects (subacute to chronic stroke) performing pointing and reaching tasks was done. Multiple and logistic regression analyses were used to estimate concurrent and discriminant validity respectively. Cutoff points for distinguishing between levels of upper limb motor impairment severity (mild, moderate to severe) were estimated using sensitivity/specificity decision plots. The criterion measure used was the Fugl-Meyer Assessment (upper limb section). RESULTS: The majority of variance in Fugl-Meyer Assessment scores was explained by a combination of trunk displacement and shoulder flexion (51%) for the pointing task and by trunk displacement alone (52%) for the reach-to-grasp task. Trunk displacement was the only variable that distinguished between levels of motor impairment severity. Cutoff points were 4.8 cm for pointing and 10.2 cm for reach-to-grasp movements. CONCLUSIONS: Movement quality kinematic variables are valid measures of arm motor impairment levels poststroke. Their use in regular clinical practice and research is justified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".