Canadian Physicians’ Use of Ultrasound in Spasticity Treatment: A National Cross-Sectional Survey
Bibliographic record
Abstract
Objective: To identify potential barriers and obstacles preventing clinicians from adopting ultrasound for spasticity management. Design: A prospective, cross-sectional national survey. Setting: Web-based platform. Participants: Thirty-six physicians and surgeons from across Canada. Interventions: Survey completion. Main Outcome Measures: The use of ultrasound in clinical spasticity practice, perceived barriers, and risks associated with its implementation. Results: In total, 36 Canadian physicians and surgeons responded. A total of 91% reported using the US in their practice. Nearly all of them used ultrasonography (US) to guide injections and reported using more than 1 guidance technique for their injections. Less than half of the survey respondents reported using the US for muscle architecture assessment or longitudinal evaluation of muscle echo intensity. A total of 47% of survey respondents reported that they believe there are disadvantages associated with US use in spasticity practice. Disadvantages included increased time requirements resulting in discomfort for the injector and patient, the risk of infection after the procedure, and the risk of needle-stick injury. The most important barrier identified was the increased time demands of US compared with other guidance techniques. Other barriers included a lack of feedback on identifying a spastic muscle compared with electrical guidance techniques, a lack of additional remuneration to complete injections under ultrasound guidance, and a lack of adequate training. Conclusions: Future educational efforts should address clinicians' lack of familiarity with US purposes outside of injection guidance. This survey has highlighted the need for a curriculum shift in spasticity education to improve physician's scanning and injection technique, to address concerns about increased time requirements for injecting under ultrasound guidance and to address perceived disadvantages from clinicians.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".