Treating Newly Diagnosed Epilepsy: The Canadian Choice
Bibliographic record
Abstract
BACKGROUND: Choosing an antiepileptic medication to treat a patient with epilepsy can be a complicated process during which the treating physician must base her or his decision on efficacy and safety of each of many available drugs. The lack of comparative studies between medications is one of the reasons. METHODS: We conducted a survey on the management of newly diagnosed epilepsy in adult patients. The surveyed were adult and pediatric neurologists with a subspecialty interest in epilepsy who were working in academic institutions or private practice across Canada. Scenarios presented were grouped in categories according to the epilepsy syndrome (absence epilepsy, juvenile myoclonic epilepsy, undetermined idiopathic generalized epilepsy, symptomatic or cryptogenic partial epilepsy, and unclassified epilepsy), the patient's gender and age. First and second step in medical treatment for status epilepticus were surveyed as well. RESULTS: Forty one of 64 experts responded the survey (responder rate of 66%). The results revealed a consensus among Canadian epileptologists that the first choice of antiepileptic medication in generalized epilepsies was between valproate in men (chosen by 88% of respondents) and lamotrigine in women. In localization-related epilepsies, carbamazepine was the preferred drug of choice (chosen by 90% of respondents). In the treatment of status epilepticus, an initial intravenous dose of lorazepam (95% of respondents), followed by a second dose of lorazepam or intravenous phenytoin in case the initial dose of lorazepam failed, were the treatments preferred.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".