Tonic Stretch Reflex Threshold as a Measure of Spasticity: Implications for Clinical Practice
Bibliographic record
Abstract
Lance's definition of spasticity states: (1) the excitability of the tonic stretch reflex threshold is the conceptual unit of measure of spasticity; (2) because spasticity is a velocity-dependent phenomenon, different velocities of stretch should be used to evaluate spasticity; and (3) measures should provide insight into the role of spasticity in disordered motor control. This article reviews studies of clinical and laboratory-based methods to evaluate spasticity. There is a lack of consensus regarding the conceptual unit that best captures spasticity. Several biomechanical variables and parameters such as stretch reflex gain, stretch reflex threshold, and/or suprathreshold phenomena are often measured alone or in combination without a unifying conceptual framework. Most studies do not establish links between spasticity and other motor deficits and adhere only partly to Lance's definition. A promising alternative to current measures is offered by the lambda model that uses the tonic stretch reflex threshold as the descriptor of spasticity. The lambda model also provides a framework in which spasticity and the presence of disorders in muscle activation can be described and explained. We introduce a portable device to measure spasticity, based on the measurement of the tonic stretch reflex threshold, which is more closely related to Lance's definition.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".