Long‐term Outcome Is Unchanged by Antiepileptic Drug Treatment after a First Seizure: A 15‐year Follow‐up from a Randomized Trial in Childhood
Bibliographic record
Abstract
PURPOSE: In 1989 we reported that 1 year of daily treatment with carbamazepine (CBZ) significantly reduced the recurrence rate after a first afebrile seizure in children compared with no treatment in a randomized open trial. We sought to determine if the long-term clinical course and rate of remission were changed by treatment starting after the first seizure. METHODS: Participants of the original trial (14 CBZ, 17 no medication) were contacted in 2001. RESULTS: Sixteen controls and 10 CBZ (84%) cases were followed up for 15 years. After randomization and follow-up, 12 controls and five treated patients had at least one more seizure (p = NS). For those with recurrences, the number of recurrences appeared to be the same in both groups. Number of medications used was also the same. Terminal remission (>2 years seizure free with or without medication) was achieved by eight (80%) of 10 CBZ and 14 (88%) of 16 controls, and terminal remission off medication in six (60%) of 10 CBZ (p = NS). CONCLUSIONS: Based on this small study with long follow-up, it appears that for children treated with CBZ after a first seizure, the subsequent clinical course and remission rates are not improved in comparison with a no-treatment strategy. Delaying treatment after a first seizure appears defensible.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".