Virtual neuromuscular ultrasound courses during <scp>COVID</scp>‐19 pandemic: Leveraging technology to enhance learning opportunities
Bibliographic record
Abstract
INTRODUCTION/AIMS: Hands-on supervised training is essential for learning diagnostic ultrasound. Unfortunately, the coronavirus disease 2019 (COVID-19) pandemic led to suspension of in-person training courses. As a result, many hands-on training courses were converted into virtual courses during the pandemic. Several reports regarding virtual ultrasound courses exist, but none has addressed virtual neuromuscular ultrasound courses, their design, or participants' views of this form of training. Therefore, the aims of this study were: (1) to determine the feasibility of conducting virtual neuromuscular ultrasound courses during the COVID-19 pandemic; and (2) to report the positive and negative aspects of the courses through the analyses of the responses of post-course surveys. METHODS: Two virtual neuromuscular ultrasound courses, basic and intermediate level, were conducted by the Egyptian Neuromuscular Ultrasound society during August 2020. Post-course, the attendees were directed to an electronic survey that consisted of eight questions. Ninety-three responses (23.8%) were obtained from the survey of the basic course and 156 responses (44.4%) were obtained from the survey of the intermediate course. RESULTS: Ninety-eight percent of the respondents to basic course surveys, and 100% of the respondents to the intermediate course survey found the courses useful or very useful. DISCUSSION: This report demonstrates the utility of virtual neuromuscular ultrasound courses for those participants willing to respond to a survey and describes a proposed design for such courses. Although hands-on supervised ultrasound training is ideal, virtual courses can be useful alternatives to in-person training when in-person interaction is restricted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".