Towards a clinically informed, data‐driven definition of elderly onset epilepsy
Bibliographic record
Abstract
OBJECTIVE: Elderly onset epilepsy represents a distinct subpopulation that has received considerable attention due to the unique features of the disease in this age group. Research into this particular patient group has been limited by a lack of a standardized definition and understanding of the attributes associated with elderly onset epilepsy. METHODS: We used a prospective cohort database to examine differences in patients stratified according to age of onset. Linear support vector machine learning incorporating all significant variables was used to predict age of onset according to prespecified thresholds. Sensitivity and specificity were calculated and plotted in receiver-operating characteristic (ROC) space. Feature coefficients achieving an absolute value of 0.25 or greater were graphed by age of onset to define how they vary with time. RESULTS: We identified 2,449 patients, of whom 149 (6%) had an age of seizure onset of 65 or older. Fourteen clinical variables had an absolute predictive value of at least 0.25 at some point over the age of epilepsy-onset spectrum. Area under the curve in ROC space was maximized between ages of onset of 65 and 70. Features identified through machine learning were frequently threshold specific and were similar, but not identical, to those revealed through simple univariable and multivariable comparisons. SIGNIFICANCE: This study provides an empirical, clinically informed definition of "elderly onset epilepsy." If validated, an age threshold of 65-70 years can be used for future studies of elderly onset epilepsy and permits targeted interventions according to the patient's age of 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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".