The online educational tool “Roadmap to <scp>EEG</scp>s” significantly improved trainee performance in recognizing <scp>EEG</scp> patterns
Bibliographic record
Abstract
OBJECTIVE: We created a framework to assess the competency-based EEG curriculum, outlined by the International League Against Epilepsy (ILAE) through a video-based online educational resource ("Roadmap to EEGs") and assessed its effectiveness and feasibility in improving trainees' knowledge. METHODS: Ten video-based e-learning modules addressed seven key topics in EEG and epileptology (normal EEG, normal variants, EEG artifacts, interictal epileptiform discharges (IED), focal seizures, idiopathic generalized epilepsy (IGE), and developmental and epileptic encephalopathies (DEE)). We posted the educational videos on YouTube for free access. Pre- and post-tests, each comprising 20 multiple-choice questions, were distributed to institution leadership and advertised on social media platforms to reach a global audience. The tests were administered online to assess the participants' knowledge. Pre- and post-test questions showed different EEG samples to avoid memorization and immediate recall. After completing the post-test, participants were asked to respond to 7 additional questions assessing their confidence levels and recommendations for improvement. RESULTS: A total of 52 complete and matched pre- and post-test responses were collected. The probability of a correct response was 73% before teaching (95% CI: 70%-77%) and 81% after teaching (95% CI: 78%-84%). The odds of a correct response increased significantly by 59% (95% CI: 28%-98%, p < .001). For participants having >4 weeks of EEG training, the probability of a correct response was 76% (95% CI: .72-.79) and 81% after teaching (95% CI: .78-.84). The odds of answering correctly increased by 44% (95% CI: 15%-80%, p = .001). Participants felt completely confident in independently interpreting and identifying EEG findings after completing the teaching modules (17.1% before vs. 37.8% after, p-value < .0001). 86.5% of participants expressed a high likelihood of recommending the module to other trainees. SIGNIFICANCE: The video-based online educational resource allows participants to acquire foundational knowledge in EEG/epilepsy, and participants to review previously learned EEG/epilepsy information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".