Response to first antiepileptic drug trial predicts health outcome in epilepsy
Bibliographic record
Abstract
PURPOSE: Failure to respond to the initial antiepileptic drug (AED) is a predictor of increased risk of pharmacoresistant epilepsy. Whether response to the first AED also predicts adverse health outcomes is unknown. METHODS: This longitudinal study compared rates of major adverse health outcomes (loss of driving privileges, unemployment, divorce/separation, injury, emergency room admission, hospitalization, and death) in 33 patients who failed the first AED (cases) and 30 patients who became seizure-free with the first AED (controls). Patient data were obtained by chart review and confirmed through a structured interview with each subject at 5-7 years after starting AED treatment. We also assessed between-group differences in quality of life, depression, and adverse AED effects by using standardized instruments completed by each subject at the end of follow-up. KEY FINDINGS: The number of major adverse health outcomes was similarly high during the first year of AED treatment [mean ± standard deviation (SD) 2.64 ± 0.99 for cases and 2.50 ± 1.14 for controls], but thereafter decreased to a greater extent in controls than in cases (p < 0.001). Controls had a higher cumulative probability of experiencing ≥1 year free from major adverse health outcomes compared to cases (p = 0.002). Two cases died during the follow-up, both of sudden unexpected death. Cases had worse quality of life ratings than controls, whereas no significant between-group differences were found for measures of depression and adverse AED effects. In a post hoc analysis limited to cases, patients who became seizure-free with subsequent AED treatments showed for the first 4 years major adverse health outcome rates similar to those recorded in patients with persisting seizures. After 4 years, however, cases who achieved late seizure freedom tended to show a more favorable outcome. SIGNIFICANCE: Patients with epilepsy failing the initial AED trial are at increased risk of experiencing adverse health outcomes, at least for the first 4 years after diagnosis. Incorporating these findings into clinical decision making may aid in reducing delays in surgical referrals for pharmacoresistant epilepsy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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; 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".