Tonic stretch reflex threshold as a measure of spasticity after stroke: Reliability, minimal detectable change and responsiveness
Bibliographic record
Abstract
OBJECTIVE: To determine inter-rater reliability, minimal detectable change and responsiveness of Tonic Stretch Reflex Threshold (TSRT) as a quantitative measure of elbow flexor spasticity. METHODS: Elbow flexor spasticity was assessed in 55 patients with sub-acute stroke by determining TSRT, the angle of spasticity onset at rest (velocity = 0°/s). Elbow flexor muscles were stretched 20 times at different velocities. Dynamic stretch-reflex thresholds, the elbow angles corresponding to the onset of elbow flexor EMG at each velocity, were used for TSRT calculation. Spasticity was also measured with the Modified Ashworth Scale (MAS). In a sub-group of 44 subjects, TSRT and MAS were measured before and after two weeks of an upper-limb intervention. RESULTS: The intraclass correlation coefficient was 0.65 and the 95% minimal detectable change was 32.4°. In the treated sub-group, TSRT, but not MAS significantly changed. TSRT effect size and standardized response mean were 0.40 and 0.35, respectively. Detection of clinically meaningful improvements in upper-limb motor impairment by TSRT change scores ranged from poor to excellent. CONCLUSIONS: Evaluation of stroke-related elbow flexor spasticity by TSRT has good inter-rater reliability. Test responsiveness is low, but better than that of the MAS. SIGNIFICANCE: TSRT may be used to complement current scales of spasticity quantification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".