Neuromuscular Ultrasound Training in Neuromuscular Fellowship Programs in Canada: Minding the Gap
Bibliographic record
Abstract
INTRODUCTION/AIMS: Neuromuscular ultrasound (NMUS) is gaining prominence as a valuable tool for diagnosing neuromuscular disorders at the point of care. Neuromuscular disorder diagnostic criteria guidelines have begun incorporating NMUS findings. As interest grows, fellowship programs must consider incorporating training into their curricula. This study evaluated the current state of NMUS training, potential barriers, and interest in training across Canadian neuromuscular fellowship programs. METHODS: A 23-question online survey was developed and distributed via email to all 10 neuromuscular fellowship program directors across Canada. RESULTS: Seven (70%) programs responded to the survey. There was general agreement among programs on the value of NMUS, however, only one (14.3%) program reported they would consider recent graduates to be competent in NMUS. Critical barriers to incorporation of NMUS training included lack of a formalized curriculum, faculty expertise and time, and equipment. Two (28.6%) programs reported that accessibility of equipment and one (14.3%) that faculty expertise was not a barrier to NMUS training. Two (28.6%) programs have local NMUS training options available to fellows (in only one program is NMUS training mandatory). All programs expressed interest in additional training opportunities, and three (43%) programs reported taking steps toward incorporating NMUS training into their curricula. DISCUSSION: NMUS training is in its infancy in Canada, with several common barriers identified across programs. There is universal interest in further NMUS training opportunities for fellows, highlighting the importance of a common approach to addressing the educational gap to support development of formalized NMUS training mechanisms in Canada.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".