Inter-rater reliability of kinesthetic measurements with the KINARM robotic exoskeleton
Bibliographic record
Abstract
BACKGROUND: Kinesthesia (sense of limb movement) has been extremely difficult to measure objectively, especially in individuals who have survived a stroke. The development of valid and reliable measurements for proprioception is important to developing a better understanding of proprioceptive impairments after stroke and their impact on the ability to perform daily activities. We recently developed a robotic task to evaluate kinesthetic deficits after stroke and found that the majority (~60%) of stroke survivors exhibit significant deficits in kinesthesia within the first 10 days post-stroke. Here we aim to determine the inter-rater reliability of this robotic kinesthetic matching task. METHODS: Twenty-five neurologically intact control subjects and 15 individuals with first-time stroke were evaluated on a robotic kinesthetic matching task (KIN). Subjects sat in a robotic exoskeleton with their arms supported against gravity. In the KIN task, the robot moved the subjects' stroke-affected arm at a preset speed, direction and distance. As soon as subjects felt the robot begin to move their affected arm, they matched the robot movement with the unaffected arm. Subjects were tested in two sessions on the KIN task: initial session and then a second session (within an average of 18.2 ± 13.8 h of the initial session for stroke subjects), which were supervised by different technicians. The task was performed both with and without the use of vision in both sessions. We evaluated intra-class correlations of spatial and temporal parameters derived from the KIN task to determine the reliability of the robotic task. RESULTS: We evaluated 8 spatial and temporal parameters that quantify kinesthetic behavior. We found that the parameters exhibited moderate to high intra-class correlations between the initial and retest conditions (Range, r-value = [0.53-0.97]). CONCLUSIONS: The robotic KIN task exhibited good inter-rater reliability. This validates the KIN task as a reliable, objective method for quantifying kinesthesia after stroke.
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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 | 0.017 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".