DIAGNOSTIC PERFORMANCE OF THE AMBULATORY EEG VERSUS ROUTINE EEG AND RISK FACTORS FOR SEIZURE RECURRENCE AMONG INDIVIDUALS WITH FIRST SINGLE UNPROVOKED SEIZURES
Bibliographic record
Abstract
Background and objectives: Routine electroencephalography (rEEG) remains central in the prognosis of seizure recurrence among individuals with a First Single Unprovoked seizure (FSUS). Furthermore, it is well-established that the presence of epileptiform discharge (ED) in the EEG increases the risk of further seizures among individuals with FSUS up to 3 times compared with individuals without such EEG changes. However, the rEEG has low sensitivity, leaving patients and clinicians without a fast and accurate tool for the prognosis of further seizures. This study aims to determine and compare the discriminative power, clinical predictive value, and global diagnostic accuracy of the ambulatory EEG compared with the first rEEG and second rEEG. This study also aims to determine risk factors for further seizures among individuals with FSUS, including ED in the ambulatory EEG. Methods: The study used a prospective cohort design with a total of 100 individuals with FSUS who underwent three modalities of EEG (first rEEG, second rEEG and Ambulatory EEG) and who were followed up for one year period. All the required information was available in this dataset, and further seizures were prospectively recorded. The three EEG (first, second rEEGs and ambulatory EEG) were interpreted by licensed neurologists recognized by the Royal College of Physicians and Surgeons of Canada and fully accredited by the Canadian Society of Clinical Neurophysiologists. Diagnosis of epilepsy was made based on clinical, neurophysiology and imaging tests following the definition of epilepsy by the International League Against Epilepsy 2014. Receiver-operating-characteristic (ROC) analysis was used to evaluate the results. Also, P a g e iii table-life and survival analysis were used to determine the risk for further seizures during the 52 weeks follow-up period. Results: We found that the ambulatory EEG’s diagnostic accuracy was better than the first and second EEG (0.79 vs. 0.51 and 0.54, respectively) in the population. Age group was a confounder in the association between seizure recurrence at 52 weeks and the presence of ED in the ambulatory EEG. The presence of ED in the ambulatory EEG increased the risk of seizure recurrence among individuals with FSUS 3.2 times when adjusted for use of antiseizure medication (ASM) and age group. Finally, other risk factors modifying the association between further seizures and the presence of ED in the ambulatory EEG included age group of >60 years (HR: 0.27 95%CI: 0.10,0.74) and the use of ASM (HR: 12.9, 95%CI: 5.6, 29.3). Conclusions: The overall diagnostic accuracy of the ambulatory EEG as a means of detecting ED among individuals with FSUS is better than the first and second rEEG. Furthermore, ED in the ambulatory EEG is a significant risk factor predicting further seizures after a single unprovoked seizure after adjusting for the use of ASM and age group. Significance: This study advanced our knowledge about the use of ambulatory EEG as an ancillary tool for predicting further seizures after FSUS and established that the presence of epileptiform activity in the ambulatory EEG is a risk factor for further seizures after adjusting for use of ASM and age group. The use of ambulatory EEG may reduce diagnostic errors and is also low-cost and better tool which can be used worldwide for more accurate diagnosis of epilepsy compared to rEEG.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".