P.075 The trend of electroencephalograph findings after starting anti-epileptic drugs during seizure assessment in children
Bibliographic record
Abstract
Background: Few studies have explored the effects of anti-epileptic drugs (AEDs) on electroencephalograph (EEG) findings during the assessment of seizure management. Although a patient may reach seizure freedom, EEG results may continue to be abnormal. Further information is required to understand the trend of EEG findings during seizure treatment. Methods: This is a retrospective study based on chart reviews. Patients who had epilepsy evaluations at the Royal University Hospital in Saskatoon between January 2012 and December 2015, were selected. The relationships among time of initiating AEDs, EEG findings, and seizure outcome on follow-ups, have been evaluated. Results: 151 patients had first seizure clinic assessments, in which 75 patients had an EEG before starting AEDs. Among the 75 patients, 54 (72%) had abnormal EEGs. From those, 38 (70.3%) patient’s EEGs became normal and 16 (29.7%) patients continued to have abnormal EEGs after the introduction of AEDs. The seizure freedom was 81.5% among those who had normal EEG on follow-up, and 43.7% of those who continued to have abnormal EEGs. Conclusions: Although patients with normal EEGs after starting AEDs may encounter a higher chance of seizure freedom, the seizure free patients with abnormal EEGs indicate that EEG is not completely sufficient in predicting seizure status.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".