Guidelines for neuromuscular ultrasound training
Bibliographic record
Abstract
Neuromuscular ultrasound has become an essential tool in the diagnostic evaluation of various neuromuscular disorders, and, as such, there is growing interest in neuromuscular ultrasound training. Effective training is critical in mastering this modality. Our aim was to develop consensus-based guidelines for neuromuscular ultrasound training courses. A total of 18 experts participated. Expert opinion was sought through the Delphi method using 4 consecutive electronic surveys. A high degree of consensus was achieved with regard to the general structure of neuromuscular ultrasound training; the categorization of training into basic, intermediate, and advanced levels; the learning objectives; and the curriculum for each level. In this study, a group of neuromuscular ultrasound experts established consensus-based guidelines for neuromuscular ultrasound training. These guidelines can be used in the development of the specialty and the standardization of neuromuscular ultrasound training courses and workshops.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".