Safety and Yield of Early Cessation of AEDs in Video-EEG Telemetry and Outcomes
Bibliographic record
Abstract
BACKGROUND: Video-electroencephalography (VEEG) telemetry is the simultaneous recording of ictal and interictal EEG pattern and paroxysmal behavior to investigate the nature of paroxysmal events. METHODS: This is a prospective study performed to asses the safety and yield of early discontinuation of antiepileptic drugs (AEDs) in the telemetry unit. Over a 2.5-year period, 50 patients that met the indications for VEEG monitoring were admitted by an epileptologist to neuro-observation units with continuous monitoring, nursing coverage and EEG technicians support during working hours and on-call thereafter. In most cases AEDs (except Phenobarbital) were discontinued in 24h. We prospectively assessed the yield and safety of the telemetry investigation as well as epilepsy surgery outcomes. RESULTS: Our monitoring answered the study question in 88% of the patients. The question was not answered in 12% of cases due to the lack of recorded events. Our results changed the management in 74% of cases and potentially improved quality of life by decreasing the AEDs consumption and number of seizures per month. Over all, 22% received epilepsy surgery and became either seizure free or their seizures became non-disabling. Our method significantly decreased the duration of hospital admission for monitoring and minimal complications occurred only in 8% of patients. CONCLUSIONS: In conclusion, our method for short VEEG monitoring has a high yield for diagnosis, minimal complications and is cost effective. These qualities, together with good surgery results validate our method for the investigation and treatment of refractory seizure cases.
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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 | 0.002 | 0.012 |
| 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) | 0.001 | 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".