Diagnostic Accuracy of the Ambulatory EEG vs. Routine EEG for First Single Unprovoked Seizures and Seizure Recurrence: The DX-Seizure Study
Bibliographic record
Abstract
Rationale: The DX-Seizure study aims to evaluate the diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratio) of the ambulatory EEG in comparison with the first routine EEG, and a second routine EEG right before the ambulatory EEG, on adult patients with first single unprovoked seizure and define the utility of ambulatory EEG in forecasting seizure recurrence in these patients after one-year follow-up. Methods: The DX-Seizure study is a prospective cohort of 113 adult patients (>18-year-old) presenting with first unprovoked seizure to the Single Seizure Clinic for evaluation. These patients will be assessed by a neurologist/epileptologist with the first routine EEG (referral EEG) and undergo a second routine EEG and ambulatory EEG. The three EEG (first routine EEG as a gold standard) will be compared and evaluated their diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios) with respect to epileptiform activity and other abnormalities. One-year follow-up of each patient will be used to assess the recurrence of seizures after a first unprovoked seizure and the utility of the ambulatory EEG to forecast these recurrences. Expected Results: We await Ambulatory EEG to increase the accuracy to detect spike-wave/seizures abnormalities to improve timely and accurate diagnosis of epilepsy in patients with single unprovoked seizures.
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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 | 0.003 | 0.008 |
| 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 | 0.000 | 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".