Enhancing Botulinum Toxin Injection Precision: The Efficacy of a Single Cadaveric Ultrasound Training Intervention for Improved Anatomical Localization
Bibliographic record
Abstract
Ultrasound guidance can enhance existing landmark-based injection methods, even through a brief and single exposure during a cadaveric training course. A total of twelve participants were enrolled in this training program, comprising nine physical medicine and rehabilitation specialists, one pediatrician, and two physician assistants. For each participant, one upper-limb muscle and one lower-limb muscle were randomly chosen from the preselected muscle group. Subsequently, participants were tasked with injecting both of their chosen cadaveric muscles with 1 mL of acrylic paint using a manual needle palpation technique, relying solely on their knowledge of anatomic landmarks. Participants then underwent a personalized, one-to-one ultrasound teaching session, lasting approximately five minutes, conducted by two highly experienced instructors. Following this instructive phase, participants were tasked with a second round of injections, targeting the same two muscles in the lower and upper limbs. However, this time, the injections were performed using anatomical landmarks and ultrasound guidance. To facilitate differentiation from the initial injections, a distinct color of acrylic paint was employed. When employing the anatomical landmark-based approach, the overall success rate for injections was 67%, with 16 out of 24 targeted muscles accurately injected. With the incorporation of ultrasound guidance, the success rate was 92%, precisely targeting 22 out of the 24 muscles under examination. There was an improvement in injection accuracy achievable through the integration of ultrasound guidance, even with minimal training exposure. Our single cadaveric ultra-sound training program contributes valuable insights to the utilization of ultrasound for anatomy training to help optimize the targeting of BoNT-A.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".