Focal nonmotor versus motor seizures: The impact on diagnostic delay in focal epilepsy
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that people with focal epilepsy experience diagnostic delays that may be associated with preventable morbidity, particularly when seizures have only nonmotor symptoms, we compared time to diagnosis, injuries, and motor vehicle accidents (MVAs) in people with focal nonmotor versus focal seizures with motor involvement at epilepsy onset. METHODS: This retrospective study analyzed the enrollment data from the Human Epilepsy Project, which enrolled participants between 2012 and 2017 across 34 sites in the USA, Canada, Europe, and Australia, within 4 months of treatment for focal epilepsy. A total of 447 participants were grouped by initial seizure semiology (focal nonmotor or focal with motor involvement) to compare time to diagnosis and prediagnostic injuries including MVAs. RESULTS: Demographic characteristics were similar between groups. There were 246 participants (55%) with nonmotor seizures and 201 participants (45%) with motor seizures at epilepsy onset. Median time to diagnosis from first seizure was 10 times longer in patients with nonmotor seizures compared to motor seizures at onset (P < .001). The number and severity of injuries were similar between groups. However, 82.6% of MVAs occurred in patients with undiagnosed nonmotor seizures. SIGNIFICANCE: This study identifies reasons for delayed diagnosis and consequences of delay in patients with new onset focal epilepsy, highlighting a treatment gap that is particularly significant in patients who experience nonmotor seizures at epilepsy onset.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".