Delphi Analysis: Optimizing Anatomy Teaching and Ultrasound Training for Botulinum Neurotoxin Type A Injection in Spasticity and Dystonia
Bibliographic record
Abstract
Our objective was to provide expert consensus on best practices for anatomy teaching and training on ultrasound-guided botulinum neurotoxin type A (BoNT-A) injection for specialists involved in treating spasticity and dystonia. Nine experts (three neurologists; six physical medicine and rehabilitation physicians) participated in a three-round modified Delphi process. Over three rounds, experts reached consensus on 15 of 16 statements describing best practices for anatomy and BoNT-A injection training. They unanimously agreed that knowledge of the target audience, including their needs and current competency, is crucial when designing training programs. Experts also agreed that alignment between instructors is essential to ensure consistency of approach over time and between regions, and that training programs should be simple, adaptable, and "hands-on" to enhance engagement and learning. Consensus was also reached for several other key areas of training program development. The best-practice principles identified by expert consensus could aid in the development of effective, standardized programs for anatomy teaching and BoNT-A injection training for the purposes of treating spasticity and dystonia. This will enhance the exchange of knowledge, skills, and educational approaches between global experts, allowing more specialists to treat important movement disorders and ultimately improving patient outcomes.
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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 | 0.171 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".