Bibliographic record
Abstract
Background and objectives: There are very few studies reporting the factors involved in or the rate of drug-resistant epilepsy (DRE) in adults with new-onset epilepsy (NOE). This prospective cohort study characterizes DRE and risk factors in a pure adult cohort with NOE or newly diagnosed epilepsy (NDE). There are very few studies reporting the factors involved in or the rate of DRE in adults with NO and NDE. Methods: Patients were selected from a prospective cohort followed between 2011 and 2018 from the Single Seizure Clinic (SSC) in Saskatoon, SK. The SSC sees patients who experience their first seizure and approximately 30% are diagnosed with epilepsy. We identified the following variables and outcomes in the cohort: age, gender, epilepsy type, seizure onset, etiology, epilepsy syndromes, EEG and imaging outcomes, and the rates of DRE. Inclusion criteria included patients with NO and NDE, age 18 years or older at time of diagnosis, and a minimum 1 year of follow‐up. Results: Ninety-five patients were included, 46 females and 49 males. Median age of onset was 33 years. Of those, 20.0% developed DRE between 2011-2018. Average time between onset and DRE diagnosis was 2.32 years. Bivariate analysis identified age, gender, and etiology as important risk factors for DRE, however these variables failed to be significant in the multivariate model. Discussion: A lower percentage of DRE was identified in this cohort of adults compared to any other published study at present. The majority of patients to develop DRE were diagnosed in the first year of follow up, showing the importance of early characterization and treatment. Similarly, a younger age of onset was shown to be a substantial indicator of the prognosis. Despite a small cohort and insignificant statistical outcomes, our findings might guide the directions of future research in this topic. Significance: The specificity of the cohort along with the outcomes identified in this study contribute valuable information about NOE in adults and the development of DRE. This study has laid the groundwork for not only a larger cohort study to be implemented in the future, but also several other studies to evaluate potential predictors such as specific imaging results as a risk factor for DRE and quality of life assessment through follow-up related to DRE risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".