Proprioceptive Thresholds Are Indicators of Upper Limb Perception After Stroke
Bibliographic record
Abstract
Background Proprioception is critical for daily activities and is often impaired after stroke. Recent studies have highlighted the prevalence of proprioceptive impairments of the upper limb in stroke; however, few studies have examined the relationship between proprioceptive impairments and motor function. Objective We examined how proprioceptive detection thresholds (movement discrimination thresholds [MDTs]) relate to existing assessments of upper limb proprioceptive, motor, and clinical function after stroke. Methods Stroke (N = 39) and control participants (N = 39) completed 5 tasks using the Kinarm Exoskeleton Lab: (1) MDT—a single-arm proprioceptive task assessing movement detection threshold, (2) Position Matching—a bilateral matching task assessing static limb position sense, (3) Kinesthetic Matching—a bilateral matching task assessing sense of limb motion, (4) Visually Guided Reaching—a task assessing upper limb motor control, and (5) Reaching without Vision—a task assessing upper limb motor control with increased reliance on proprioceptive feedback. Results Stroke participants were significantly impaired on all robotic tasks compared to controls. We found that MDT was correlated with bilateral matching robotic tasks of proprioception, including Position Matching (ρ = .64, P < .001) and Kinesthetic Matching (ρ = .56, P < .001). However, MDT was not significantly correlated with robotic tasks of motor control or clinical measures. Conclusions MDT, a single-arm measure that reduces motor requirements in proprioceptive testing, was significantly correlated to existing robotic measures of proprioception. MDT is capable of measuring impairments in perception that may be independent from impairments in action-based motor function.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".