Spasticity Management Teams, Evaluations, and Tools: A Canadian Cross-Sectional Survey
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to determine the physical evaluations and assessment tools used by a group of Canadian healthcare professionals treating adults with spasticity. METHODS: A cross-sectional web-based 19-question survey was developed to determine the types of physical evaluations, tone-related impairment measurements, and assessment tools used in the management of adults with spasticity. The survey was distributed to healthcare professionals from the Canadian Advances in Neuro-Orthopedics for Spasticity Congress database. RESULTS: Eighty study participants (61 physiatrists and 19 other healthcare professionals) completed the survey and were included. Nearly half (46.3%, 37/80) of the participants reported having an inter- or trans-disciplinary team managing individuals with spasticity. Visual observation of movement, available range of motion determination, tone during velocity-dependent passive range of motion looking for a spastic catch, spasticity, and clonus, and evaluation of gait were the most frequently used physical evaluations. The most frequently used spasticity tools were the Modified Ashworth Scale, goniometer, and Goal Attainment Scale. Results were similar in brain- and spinal cord-predominant etiologies. To evaluate goals, qualitative description was used most (37.5%). CONCLUSION: Our findings provide a better understanding of the spasticity management landscape in Canada with respect to staffing, physical evaluations, and outcome measurements used in clinical practice. For all etiologies of spasticity, visual observation of patient movement, Modified Ashworth Scale, and qualitative goal outcomes descriptions were most commonly used to guide treatment and optimize outcomes. Understanding the current practice of spasticity assessment will help provide guidance for clinical evaluation and management of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".