Factors Predictive of Outcome in Childhood Epilepsy
Bibliographic record
Abstract
To identify early predictive factors of outcome in childhood epilepsy, the case records of all children with new-onset epilepsy presenting to a single neurology practice over a 10-year interval were reviewed. Only children with more than 2 years of follow-up were included. Cox regression analysis was used to identify factors predictive of remission (successful cessation of medication). One hundred ninety-six children (mean age 7.6 +/- 3.7 years at first seizure, mean follow-up 55 +/- 30 months) were identified. Ninety-eight of 196 children (50%) had an idiopathic epilepsy, 63 of 196 (32.1%) had cryptogenic epilepsy, and 35 of 196 (17.9%) had remote symptomatic epilepsy. At final assessment, 52.6% were in remission, 12.8% had a poor outcome (recurrent seizures on therapeutic antiepileptic drug levels within 6 months prior to the final assessment), and 6.9% were intractable (more than one seizure/month over 1 year with failure of three or more anticonvulsants). One year after initiating treatment, factors associated with a lower probability of remission included seizure recurrence in the 6- to 12-month interval after therapy initiation (hazard ratio 0.24), multiple seizure types (hazard ratio 0.40), and mental retardation at onset (hazard ratio 0.19). Factors predictive of a poor outcome included seizure recurrence in the 6- to 12-month interval after therapy initiation (odds ratio 21.6), more than one seizure type (odds ratio 8.9), and global developmental delay at onset (odds ratio 8.9). Factors predictive of intractability included multiple seizure types (hazard ratio 6.5), mental retardation at onset (hazard ratio 7.2), and seizure recurrence in the first 6 to 12 months of treatment (hazard ratio 70). It appears that response in the first 6 to 12 months on antiepileptic medication is predictive of outcome. Clinical features of the underlying epilepsy and concurrent neurologic conditions were independently associated with intractability and a lower probability of remission.
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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 | 0.007 |
| 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 | 0.001 |
| 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".