The ILAE definition of drug resistant epilepsy and its clinical applicability compared with “older” established definitions
Bibliographic record
Abstract
SUMMARY Background. Early identification of potential epilepsy surgery candidates is essential to the treatment process. Aim. To evaluate the clinical applicability of the ILAE definition of drug resistant epilepsy and its potential in identifying surgical candidates earlier compared to three established “older” definitions of drug resistant epilepsy. Material and Methods. Retrospective analysis of 174 patients who underwent epilepsy surgery between 1998 and 2009. Clinical factors and course of disease were extracted from patients' charts. Drug resistant epilepsy was classified according to four definitions and the time until fulfillment of criteria compared. Results. Mean time to fulfillment of criteria of drug resistant epilepsy ranged from 11.8 (standard deviation (SD) 9.8) to 15.6 years (SD 11.3). Time to drug resistance was significantly longer applying the only definition, requiring failure of three antiepileptic drugs (AEDs) (Canada definition), whereas time to fulfillment of all other definitions did not differ. Fifty percent of all patients experienced a seizure free period of ≥1 year prior to being classified as drug resistant, 13% entered another 1-year remission after fulfilling any criteria for drug resistance. Conclusion. We conclude that the ILAE definition identifies drug resistant epilepsy, with similar latency like two of three formerly used definitions. It is an easy applicable tool to minimize the delay of referral to a specialized center. Intermittent remissions delay assessment of drug resistance for all definitions and 13% of patients enter a remission despite established drug resistance.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".