A practical booklet for ultrasound-guided botulinum toxin injections
Bibliographic record
Abstract
In the last 15 years, the use of ultrasound to guide botulinum neurotoxin type A injections has been advocated by many authors, with growing evidence showing the benefits of using ultrasound guidance to improve the efficacy of injections. Patients with spasticity may show severely altered postures, atrophy and fibrotic modifications of target muscles, leading to significant challenges in recognising and differentiating between the muscles to be injected or not. At present time, there are no available books with images that clarify how to identify and inject muscles in patients showing these problems. Another problem we considered is the accessibility, from an economic standpoint, of medical books for clinicians in low-income countries. We have created a practical booklet to help clinicians acquire the confidence and expertise needed to administer US-guided injections in patients with severe spasticity, combining the experience of more than 10 years in training clinicians in ultrasound-guided injections. We utilised our experience to create a method that offers a consistent way to recognise muscles, even in challenging conditions. The aim of our booklet is to offer a reliable technique for identify and target muscles in patients with altered muscular structure and pathological postures due to spasticity, relying on easily identifiable anatomical structures such as bones, vessels or nerves, or "iconic" patterns that can easily be learned and remembered. We have provided images and anatomical schemes, as well as ergonomic clinical pearls, to help clinicians providing reliable ultrasound-guided injections. To reduce barriers to education, this booklet is be distributed for free without any royalties.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.236 | 0.158 |
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".