Expert opinions on pediatric <scp>EEG</scp> training for non‐epilepsy specialists in sub‐Saharan Africa
Bibliographic record
Abstract
Ideally, pediatric electroencephalograms (EEGs) should be performed by accredited neurophysiology technologists and interpreted by specialists trained in epileptology However, low- and middle-income countries (LMICs) lack such specialists. AIM: To collate expert consensus on essential curriculum content for non-epilepsy specialists in EEG interpretation and safe post-training practice. METHOD: A qualitative study on pediatric EEG training curricula needs was designed in collaboration with an adult education specialist. Data were collected via interviews from 15 epilepsy experts with training experience across high- to low-income settings. Thematic analysis was used to identify sub-themes. The experts voted on the key statements in a two-round Delphi to ascertain consensus. RESULTS: Twelve aspects of pediatric EEG training were identified and categorized thematically: relevance; exposure to pediatrics; focus on pediatrics; barriers; resource-limited setting; entry skills; best pedagogy; assessment; critical skills; reinforcement of skills; training model; and recommendations. CONCLUSION: This study was driven by the inadequate access to training in pediatric EEG for non-epilepsy specialists, which is further exacerbated by the lack of epileptologists and neurophysiologists. The outcomes from the expert consensus opinions promoted consolidation, adaptation, and evolution of existing models that are viable for practice and to be used worldwide. The Delphi consensus demonstrated alignment among regionally located specialists towards the promotion of effective and maintained training for non-epilepsy specialists, as well as highlighting barriers that should be considered and addressed.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".