Education Research: EEG Education in Child Neurology and Neurodevelopmental Disabilities Residencies
Bibliographic record
Abstract
Background and Objectives: In the United States, many child neurologists (CNs) and neurodevelopmental disability (NDD) specialists who read EEGs in clinical practice had no additional EEG training other than what was received during residency. This practice highlights the importance of ensuring that CN/NDD residents achieve EEG competence before graduation. However, prior survey-based evidence showed that roughly a third of graduating CN residents in the United States do not feel confident interpreting EEGs independently. As part of a needs assessment, we conducted a descriptive study characterizing EEG practices in CN and NDD residency programs in the United States and Canada. Methods: A 30-question e-survey focused on characteristics of residency programs and their EEG teaching practices was sent to all 88 CN and NDD residency program directors listed in the Accreditation Council for Graduate Medical Education, Child Neurology Society, and Canadian Residency Matching Service websites. Results: = 0.007). Barriers to EEG education were reported by 28% of the programs; the most common barrier identified was insufficient EEG exposure. Possible solutions were primarily related to increasing quality and quantity of EEG exposure. Almost two-thirds of programs reported not using objective measures to assess EEG competence. Discussion: Our results characterize resident EEG education in a third of CN/NDD residency programs in the United States and Canada. We suggest that residency leaderships consider standardization of EEG learning along with establishment and implementation of objective measures in training requirements and competence assessment.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".