Proposed Pathway for the Utilization of Pediatric Ambulatory EEG
Bibliographic record
Abstract
PURPOSE: The clinical utility of pediatric ambulatory-EEG (A-EEG) has been studied for decades, but limited information exists regarding which variables influence its utility. The authors aimed to evaluate clinical/EEG variables that may influence A-EEG yields and to develop a pathway for A-EEG utilization in children. METHODS: Single-center retrospective review of A-EEGs performed from July 2019 to January 2021 in a tertiary referral center. The primary outcome was whether the A-EEG test successfully answered the referring physician's clinical question or influenced therapy. When it did, the A-EEG test was deemed useful. Clinical and EEG variables were assessed for their ability to predict utility. Further, the literature review generated 10 relevant prior studies whose details were used to generate a pathway for A-EEG utilization in children. RESULTS: One hundred forty-two A-EEG studies were included (mean age 8.8 years, 48% male patients, mean A-EEG duration 33.5 hours). Overall, A-EEG was considered useful in 106 children (75%) but heavily influenced by A-EEG indication. Specifically, it was deemed useful for 94% of patients evaluated for electrical status epilepticus in slow-wave sleep, 92% of those evaluated for interictal/ictal burden, and 63% of those undergoing spell classification. The test indication (P < 0.001), a diagnosis of epilepsy (P = 0.02), and an abnormal routine EEG (P = 0.04) were associated with A-EEG test utility, although the multivariate analysis confirmed the test indication as the only independent outcome predictor of A-EEG. CONCLUSIONS: Pediatric A-EEG is extremely useful for evaluating electrical status epilepticus in slow-wave sleep and interictal/ictal burden and is often helpful for spell classification. Among all clinical and EEG variables analyzed, the test indication was the only independent outcome predictor of obtaining a helpful A-EEG.
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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 | 0.000 | 0.004 |
| 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 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".