The Diagnostic Accuracy of Prolonged Ambulatory Versus Routine EEG
Bibliographic record
Abstract
Prolonged ambulatory electroencephalography (paEEG) is increasingly used in clinical practice but its diagnostic accuracy relative to that of routine EEG (rEEG) remains uncertain. We examined a consecutive sample of 72 individuals who had undergone 32-channel paEEG immediately after an rEEG, creating perfectly matched EEG samples. Each recording was prospectively assessed for epileptiform discharges (ED) and nonepileptiform abnormalities. The median paEEG duration was 22.5 hours (interquartile range: 22.0-23.0). The sensitivity of paEEG was 2.23 times greater than that of rEEG [sensitivity ratio: 2.23 (95% CI=1.49-3.34)] if a positive test was limited to the presence of epileptiform discharges. This benefit of paEEG versus rEEG was no longer evident if the definition of a positive test included nonepileptiform abnormalities (sensitivity ratio 1.26; 95% CI=1.02-1.55). The specificity of the 2 tests was not evidently different (specificity ratio 0.67; 95% CI=0.17-2.67). Twenty-six percent of paEEG recorded epileptic seizures while none of the rEEG did (absolute difference 26.0% (95% CI=11.8-40.2). Our findings quantify the benefit of 32-channel paEEG, relative to rEEG, and support its role in the diagnosis and characterization of epilepsy.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".