Tonic Stretch Reflex Threshold as a Measure of Ankle Plantar-Flexor Spasticity After Stroke
Bibliographic record
Abstract
BACKGROUND: Commonly used spasticity scales assess the resistance felt by the evaluator during passive stretching. These scales, however, have questionable validity and reliability. The tonic stretch reflex threshold (TSRT), or the angle at which motoneuronal recruitment begins in the resting state, is a promising alternative for spasticity measurement. Previous studies showed that spasticity and voluntary motor deficits after stroke may be characterized by a limitation in the ability of the central nervous system to regulate the range of the TSRT. OBJECTIVE: The study objective was to assess interevaluator reliability for TSRT plantar-flexor spasticity measurement. DESIGN: This was an interevaluator reliability study. METHODS: In 28 people after stroke, plantar-flexor spasticity was evaluated twice on the same day. Plantar-flexor muscles were stretched 20 times at different velocities assigned by a portable device. Plantar-flexor electromyographic signals and ankle angles were used to determine dynamic velocity-dependent thresholds. The TSRT was computed by extrapolating a regression line through dynamic velocity-dependent thresholds to the angular axis. RESULTS: Mean TSRTs in evaluations 1 and 2 were 66.0 degrees (SD=13.1°) and 65.8 degrees (SD=14.1°), respectively, with no significant difference between them. The intraclass correlation coefficient (2,1) was .851 (95% confidence interval=.703, .928). LIMITATIONS: The notion of dynamic stretch reflex threshold does not exclude the possibility that spasticity is dependent on acceleration, as well as on velocity; future work will study both possibilities. CONCLUSIONS: Tonic stretch reflex threshold interevaluator reliability for evaluating stroke-related plantar-flexor spasticity was very good. The TSRT is a reliable measure of spasticity. More information may be gained by combining the TSRT measurement with a measure of velocity-dependent resistance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".