Outcome of Absence Epilepsy With Onset at 8-11 Years of Age: Watershed Ages When Syndromes Overlap
Bibliographic record
Abstract
Introduction: Absence seizures occur in various epilepsy syndromes, including childhood and juvenile absence epilepsy and juvenile myoclonic epilepsy. When children present with absence seizures at ages when syndromes overlap, initial syndrome designation is not always possible, making early prognostication challenging. For these children, the study objective is to determine clinical and initial electroencephalograph (EEG) findings to predict the development of generalized tonic-clonic seizures, which is a factor that affects outcome. Methods: Children with new-onset absence seizures between 8 and 11 years of age with at least 5 years of follow-up data were studied through the review of medical records and initial EEG tracings. Results: Ninety-eight patients were included in the study. The median age of absence seizure onset was 9 years (interquartile range [IQR] = 8.00, 10.00) and follow-up was 15 years (IQR = 13.00, 18.00). Forty-six percent developed generalized tonic-clonic seizures and 20% developed myoclonic seizures. On multiple regression analysis, a history of myoclonic seizures, anxiety, as well as bifrontal slowing and mild background slowing on initial EEG ( P < .05) were associated with generalized tonic-clonic seizures. Although not statistically significant, a shorter duration of shortest EEG burst on baseline EEG was also associated with generalized tonic-clonic seizures. Conclusion: On initial EEG, bifrontal and background slowing and myoclonic seizures and anxiety are associated with developing generalized tonic-clonic seizures, which is of prognostic significance when early syndrome designation is difficult.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".