Ambulatory Electroencephalography (EEG) in Children: Diagnostic Yield and Tolerability
Bibliographic record
Abstract
Sixty-four children, aged 0-17 years, undergoing ambulatory electroencephalography (EEG) were prospectively recruited during a 12-month period. The diagnostic yield of ambulatory electroencephalography was determined for each of the following groups: group 1: differentiation of seizures from nonepileptic events; group 2: determination of seizure/interictal discharge frequency; and group 3: classification of seizure type or localization. The ambulatory electroencephalography answered the clinical question in 61% of group 1 (27/44) and 100% of groups 2 (16/16) and 3 (4/4). Of 44 cases in Group 1, clinical events were recorded in 61%; the ambulatory electroencephalography result changed the diagnosis from epileptic to nonepileptic or vice versa in 27%. When clinicians suspected that events were epileptic, ambulatory electroencephalography changed the clinical impression in 50%, whereas when events were suspected to be nonepileptic, ambulatory electroencephalography confirmed that impression in 83%.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".