Awareness and driving safety during awake interictal epileptiform discharges in idiopathic generalised epilepsies: A systematic review
Bibliographic record
Abstract
Idiopathic generalised epilepsies (IGEs) are a family of epileptic syndromes that commonly occur in childhood or adolescence and can persist into adulthood. Whilst people with IGE may consider themselves seizure free, they often experience interictal epileptiform discharges (IEDs) that may be associated with unrecognised periods of impaired awareness. As such, the presence of long IEDs associated with unrecognised loss of awareness pose a major challenge in allowing these individuals to drive safely, particularly since not all IEDs are associated with impaired awareness. Here we conduct a systematic review of the literature to characterise factors associated with impaired awareness during IEDs in adults. This review includes articles written in English and was limited to patients with IGE ≥16 years with evidence of IEDs on EEG. The outcomes assessed included electroclinical descriptions of IED manifestations and fitness to drive assessments in people with IGE. The systematic literature search yielded 6 studies that met the inclusion criteria. A quality assessment of the cohort studies included was conducted using a modified Newcastle Ottawa Scale. Prior research has utilised driving simulations and prolonged video EEG monitoring for fitness to drive assessments delivering positive outcomes. They have shown that the clinical manifestation of IEDs is dependent upon the discharge duration. Furthermore, it has been shown that IED morphology can be described as generalised spike or polyspike wave discharges. Additionally, expert opinion has demonstrated significant variability in practice concerning driving clearance for patients with IGEs, with only a minority utilising prolonged EEG monitoring. This review highlights our current inability to reliably predict the cognitive status of a people with IGE during IEDs. However, emerging research on the use of machine learning algorithms to analyse IED waveforms appears promising, offering a potential solution to this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".