Intra- and inter-tester reliability of spasticity assessment in standing position in children and adolescents with cerebral palsy using a paediatric exoskeleton
Bibliographic record
Abstract
BACKGROUND: The L-STIFF tool of the Lokomat evaluates the hip and knee flexors and extensors spasticity in a standing position. It moves the lower limb at a controlled velocity, measuring joint resistance to passive movements. Since its reliability in children with cerebral palsy remains unknown, our goal was to evaluate the relative and absolute reliability of L-STIFF in children with cerebral palsy. METHODS: Reliability was determined in 16 children with cerebral palsy by two experienced therapists. The changes in resistive torque in hip and knee in both flexion and extension were measured. Relative and absolute reliability were estimated using the intraclass correlation coefficient, standard error of measurement, and minimal detectable change. Reliability was assessed on three levels: (1) intra- and (2) inter-tester within session, and (3) intra-tester between sessions. RESULTS: ≤ 0.01), standard error of measurement ranged from 0.012 to 0.029 Nm/° (i.e., 6-39%), and minimal detectable change ranged from 0.033 to 0.082 Nm/°. L-STIFF reliability was better during fast and medium movement speeds compared to slow speeds. CONCLUSIONS: The assessment tool L-STIFF is a promising tool for quantifying lower limb spasticity in children with cerebral palsy in a standing position. However, the results should be interpreted carefully.Implications for RehabilitationL-STIFF is a promising tool for evaluating lower limb spasticity in standing position.A special care must be given to the installation and alignment of the participant into the Lokomat to minimize erroneous spasticity measurement.Relative standard error of measurement and minimal detectable change should be used to analyze changes spasticity.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".