Educating the Next Generation of Neuromuscular and Electrodiagnostic Practitioners: Challenges and Opportunities
Bibliographic record
Abstract
Today's neuromuscular (NM) and electrodiagnostic medicine (EDM) trainees learn very differently from those of times past. There are a variety of new tools available to them, with nearly ubiquitous access to information, technology, and social media, along with high expectations regarding immediate access to information. At the same time, they require assistance honing their reflective and critical thinking skills. Educators are encouraged to develop explicit goals of training and to consider new teaching methods such as the flipped classroom and the one-minute preceptor. Use of humor in education, when appropriate, can help with information retention and managing stress and emotion in the healthcare team. Despite inherent barriers, educators need to fail learners who do not meet expectations to benefit both learners and patients. Self-assessment examinations (SAEs) can provide very valuable feedback for both individuals and programs, as well as the field. Those who perform poorly on SAEs have a low chance of passing a subsequent certification examination. Performance on the electrodiagnostic SAE improves with increasing numbers of patient studies, not leveling out until roughly 300-400 studies have been completed; this suggests a possible benchmark for training. Poor performance on initial certification examinations is strongly correlated with the risk of subsequent state medical board disciplinary action; such individuals will want to make a special effort to stay informed and up to date. After initial certification examinations, ongoing longitudinal assessment programs can combine formative and summative assessments for both learning and certification.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".