Quantitative Assessment of Limb Position Sense Following Stroke
Bibliographic record
Abstract
BACKGROUND: Impairment of position sense of the upper extremity (UE) may impede activities of daily living and limit motor gains after stroke. Most clinical assessments of position sense rely on categorical or ordinal ratings by clinicians that lack sensitivity to change or the ability to discriminate subtle deficits. OBJECTIVE: Use robotic technology to develop a reliable, quantitative technique with a continuous scale to assess UE position sense following stroke. METHODS: Forty-five patients recruited from an inpatient stroke rehabilitation service and 65 age-matched healthy controls performed an arm position matching task. Each UE was fitted in the exoskeleton of a KINARM device. One UE was passively placed in one of 9 positions, and the subject was told to match his or her position with the other UE. Patients were compared with statistical distributions of control data to identify those with deficits in UE position sense. Test-retest sessions using 2 raters established interrater reliability. RESULTS: Two thirds of left hemiparetic and one third of right hemiparetic patients had deficits in limb position sense. Left-affected stroke subjects demonstrated significantly more trial-to-trial variability than right-affected or control subjects. The robotic assessment technique demonstrated good interrater reliability but limited agreement with the clinical thumb localizing test. CONCLUSIONS: Robotic technology can provide a reliable quantitative means to assess deficits in limb position sense following 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".