Understanding the evolution of epilepsy – the value of collecting longitudinal data in the setting of a first seizure clinic
Bibliographic record
Abstract
Objective: Our knowledge of disease mechanisms in epilepsy is biased by findings originating from cross-sectional studies and advanced stages of epilepsy. We provide a new perspective by collecting systematically longitudinal data from patients who present in early stages (ES). Methods: The Halifax First Seizure Clinic, founded in 2008, uses a comprehensive multimodal data basis addressing clinical presentation, neuroimaging, EEG findings, genetics, cognition, comorbidities, social parameters, and life style. Follow-up visits are 6, 12 and 24 months. Results: Out of 575 patients we identified 3 subgroups 1) Strictly first seizure, n=187, 2) New-onset epilepsy (> 1 seizure < 12 months), n=149, and 3) Newly-diagnosed epilepsy (seizures > > 12 months), n=50. 189 patients were excluded (not proven seizure or other conditions e.g syncope etc.). Our interim analyses suggest: A) pharmacoresistance presents in highly diverse patterns and is rarer than expected in ES, B) Congenital malformations seem to have an excellent treatment prognosis in ES, C) Marijuana consumption is significantly more prevalent at initial assessment (comparison general population), D) Preceding psychiatric comorbidities associated with reduced amygdalar volume may be a predictor for seizure recurrence. Conclusions: The preliminary and illustrative findings of our pilot study challenge current concepts of disease evolution in epilepsy.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".