Clinical Relevance of the Tonic Stretch Reflex Threshold and μ as Measures of Upper Limb Spasticity and Motor Impairment After Stroke
Bibliographic record
Abstract
BackgroundAfter a central nervous system lesion, the ability to control muscle activation and relaxation in specific joint ranges may be impaired. The underlying mechanism of this sensorimotor impairment is related to a decreased ability to regulate the tonic stretch reflex threshold (TSRT) through descending and peripheral control processes. In dynamics, the reflex threshold and its velocity-sensitivity (μ) describe how movement in specific upper limb (UL) joint ranges is impaired after stroke.ObjectiveTo examine the relationships between measures of elbow flexor impairment using TSRT and μ, and clinical scores of spasticity and motor function. We hypothesized that TSRT and μ would be related to clinical spasticity and motor impairment scores in patients with acute and chronic stroke.MethodsTSRT, μ, and clinical data of the resistance to passive movement (Modified Ashworth Scale) and UL motor function (Fugl-Meyer Assessment [FMA]) were collected from 120 patients. Relationships between variables were determined using simple correlations and multiple regression analysis.ResultsTSRT and μ explained 72.0% of the variance in the FMA of the Upper Extremity [FMA-UE] describing only in-synergy and out-of-synergy movements and reflex function. TSRT explained 68.7% of the variance in the total score of the FMA-UE.ConclusionsThis study shows for the first time, a significant relationship between deficits in TSRT regulation and μ with UL motor impairment after stroke. TSRT and μ may be valuable clinical biomarkers of sensorimotor impairment for monitoring spontaneous or treatment-induced motor recovery.
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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 | 0.000 | 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".