The term “epilepsy in the elderly” is conceptually irrelevant and needs to be replaced by an etiology-driven classification system in the aging brain
Bibliographic record
Abstract
Objective: “Epilepsy in the elderly (EE)” is considered a homogeneous, benign syndrome in patients aged > 60 years, with cerebrovascular disease as the most frequent etiology. We challenge this concept by comparing EE and middle-aged adults with epilepsy (MAE). Methods: We compared: 1) seizure dynamics, 2) MRI lesions, 3) EEG findings and 4) treatment course in EE and MAE at the Halifax First Seizure Clinic. Results: 48 EE patients aged > 60.2 years (median 66.9 years). 31 MAE patients aged 50.2 – 59.6 years (median 55.1 years). Seizure dynamics in EE/MAE included first seizure in 50/54.8%, new onset epilepsy (new seizures within 12 months) in 43.8/35.5%, newly diagnosed epilepsy (seizures for >> 12 months) in 9.6/6.3%. First seizure evolved into new onset epilepsy in 12.5/3.2%. MRI in EE/MAE was normal in 22.5/27.6% or showed microangiopathy (25/38.5%), atrophy (10/15.4%), tumors (7.5/11.5%), vascular malformations (7.5/3.8%), hippocampal pathologies (0/3.8%), infarcts (12.5/0%). EEG in EE/MAE was normal in 64.4/65.5% or showed diffuse (6.6/3.5%) or focal slowing (8.8/7%), generalized (4.3/13.7%) or focal (15.4/10.4%) epileptiform activity. At 12 months, 87% of EE and 93.8% of MAE were seizure-free. Conclusions: EE and MAE show similar heterogeneity. We propose an etiology-driven classification of epilepsy syndromes in the aging brain.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".