PREDICTING OUTCOME OF CHILDHOOD ABSENCE SEIZURES
Bibliographic record
Abstract
Objectives: Absence seizures outcome remains poorly delineated. Long-term remission occurs in two-thirds of cases. The objective of this study was to determine the clinical variables at diagnosis which could identify a person who would be seizure free for greater than one year at 18 to 24 months post-diagnosis. Methods: Case ascertainment was through EEG records of the Children's Hospital of Eastern Ontario. Inclusion criteria were between 1 and 18 years of age, history of absence seizures, EEG with general spike wave, and diagnosis and antiepileptic medication initiated by a pediatric neurologist. Exclusion criteria included traumatic head injury or presence of neuro-degenerative disorder. Retrospective chart review for age at onset, duration of symptoms prior to diagnosis, time of first remission, seizure description, family history, school status, and neurological examination was done. Independent blinded re-reading of initial EEG tracing was done. Results: 65 cases were included. 58.5% were female. Mean age at diagnosis was 6.93 years. 50.8% had only absence attacks. 32.5%had family history of afebrile seizures. 90.8% had normal cognition. 45% were seizure free for greater than one year 18months after diagnosis. Absence only seizures, history of febrile seizures and shorter time to remission were associated with good outcome. Conclusion: Variables predictive of being seizure free for over one year at 18 months after diagnosis of generalized absence seizure disorder included simple absence attacks, history of febrile seizures and shorter time to 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".