Psychometric Properties of a New Measure of Upper Limb Performance in Post-Stroke Individuals: Trunk-Based Index of Performance
Bibliographic record
Abstract
Background Several measures of upper limb (UL) motor tasks have been developed to characterize recovery. However, UL performance and movement quality measures in isolation may not provide a true profile of functional recovery. Objective To investigate the measurement properties of a new trunk-based Index of Performance (IPt) of the UL combining endpoint performance (accuracy and speed) and movement quality (trunk displacement) in stroke. Methods Participants with stroke (n = 25, mean time since stroke: 18.7 ± 17.2 months) performed a reaching task over 3 evaluation sessions. The IPt was computed based on Fitts’ Law that incorporated endpoint accuracy and speed corrected by the amount of trunk displacement. Test–retest reliability was analyzed using intraclass correlation coefficient (ICC) and Bland–Altman plots. Standard error of measurement (SEM) and Minimal Detectable Change (MDC) were determined. Validity was investigated through the relationship between IPt, Fugl–Meyer Assessment (FMA-UE), and Action Research Arm Test (ARAT), as well as the ability of IPt to distinguish between levels of UL motor impairment severity. Results Test–retest reliability was excellent (ICC = .908, 95% CI: 0.807-0.96). Bland–Altman did not show systematic differences. SEM and MDC 95 were 14% and 39%, respectively. Construct validity was satisfactory. The IPt showed low-to-moderate relationships with FMA-UE ( R 2 ranged from .236 to .428) and ARAT ( R 2 ranged from .277 to .306). IPt scores distinguished between different levels of UL severity. Conclusions The IPt showed evidence of good reliability, and initial validity. The IPt may be a promising tool for research and clinical settings. Further research is warranted to investigate its validity with additional comparator instruments.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".