Bibliographic record
Abstract
OBJECTIVE: To determine how specific early EEG abnormalities correlate with clinical outcome on long-term follow-up for neurological and epilepsy outcomes. Our aim was to revisit how early EEG abnormalities should be weighed for prognosis. METHODS: This is a retrospective study of 358 infants who had EEGs taken between 3 and 12 months of age and subsequent clinical assessment between 4 to 18 years of age. RESULT: Of the 358 infants, 215 had unfavorable neurological outcome (UNO), and 234 had epilepsy on follow-up. Breakdown: 117 had major abnormal EEG background of which 86% had UNO and 75% had epilepsy. One hundred had abnormal sleep potentials of which 89% had UNO and 80% had epilepsy. One hundred seventy-five had interictal epileptiform activity of which 80% had UNO and epilepsy. Sixty had ictal epileptiform activity of which 90% had UNO and 86% had epilepsy. One hundred ninety-two had markedly abnormal overall EEG impression of which 80% had UNO and 79% had epilepsy. CONCLUSIONS: Occurrence of significant EEG abnormalities in the first year was clearly associated with UNO and epilepsy. Current views that certain EEG abnormalities (e.g., interictal spike) may have little prognostic significance would need to be revisited.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".