Predicting drug resistance in adult patients with generalized epilepsy: A case–control study
Bibliographic record
Abstract
OBJECTIVES: Using an adult cohort of patients with generalized epilepsy, we aimed to identify risk factors for development of drug-resistant epilepsy (DRE), which if identifiable would allow patients to receive earlier treatment and more specifically individualized treatment plans. METHODS: For the case-control study, 118 patients with generalized epilepsy (GE) between the ages of 18 and 75 were included after selection from a database of 800 patients referred from throughout the Saskatchewan Epilepsy Program. Definitions were used in accordance with ILAE criteria. The odds ratio and its confidence interval were calculated. We performed a logistic regression analysis. RESULTS: Forty-four (37%) patients fulfilled the definition of DRE (cases), and seizures in 74 (63%) patients were not intractable (controls). Patients with DRE were significantly younger than the controls at the onset of epilepsy (6.6 vs. 18.8 years, p=<0.001). Significant variables on univariate analysis were the following: epilepsy diagnosed prior to 12 years (OR: 12.1, CI: 4.8-29.9, p<0.001), previous history of status epilepticus (OR: 15.1, CI: 3.2-70.9, p<0.001), developmental delay (OR: 12.6, CI: 4.9-32, p<0.001), and cryptogenic epilepsy (OR: 10.5, CI: 3.9-27.8, p<0.001). Our study showed some protective factors for DRE such as a good response to first AED, idiopathic etiology, and history of febrile seizures. In the logistic regression analysis, two variables remained statistically significant: developmental delay and more than one seizure type. CONCLUSION: Our study has identified a set of variables that predict DRE in patients with generalized epilepsy. Risk factors identified in our study are similar to those previously identified in pediatric studies, however, our study is specifically tailored to adult patients with generalized 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".