Bibliographic record
Abstract
The relatively high percentages of patients with epilepsy, in whom seizures are uncontrolled in spite of optimal antiepileptic drug use, lead to continuous struggles to improve the treatment of epilepsy. The advances in defining the genetic basis of epilepsy can potentially lead to better understanding of the disorder as well as to more effective treatment. An example is the finding of SCN1A gene mutations in association with a large spectrum of neurological diseases, from generalized epilepsy with febrile seizures plus (GEFS +) to severe myoclonic epilepsy of infancy and to vaccine-induced encephalopathy and Rasmussen encephalitis, Panayiotopoulos syndrome and familial hemiplegic migraine. In parallel, throughout the world, imaging modalities of very high technology are being used to define the epileptogenic focus. A description from The Hospital for Sick Children in Toronto, of a topographic movie of high frequency oscillations on the brain surface, which allows visualization of the dynamic ictal changes, is remarkable. The ketogenic diet is a significant treatment option. The John Freeman Epilepsy Center in Johns Hopkins Hospital leads the way in using the diet in very young infants, including West syndrome. The vagus nerve stimulation is being used as another relatively safe and effective treatment, while epilepsy surgery continues to be applied. Better matching of patients to each modality can be expected with increased success in seizure control.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.023 |
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".