Long-Term Clinical Outcome of Neonatal EEG Findings
Bibliographic record
Abstract
The aim of the study is to determine how specific EEG findings during neonatal period correlate with clinical outcome on follow-up. This is a retrospective study of 118 term newborns who had EEG in the first month of life and subsequent clinical assessment between 4 and 16 years. Clinical neurologic outcome was classified into "favorable" when patients had no or only mild limitation in assessment, "unfavorable" when patients had moderate to severe abnormalities in assessment, and "epilepsy" when patients had seizures. Of the 118 neonates, 36 (30.5%) had favorable and 82 (69.5%) had unfavorable outcome; 89 (75.4%) had epilepsy and 28 (23.7%) had not. Sixty-seven (57%) had abnormal EEG background of which 56 had both unfavorable outcome and epilepsy; 102 (86%) had sharp transient discharges of which 75 had unfavorable outcome; 20 (17%) had ictal epileptiform discharges of which 18 had unfavorable outcome; 98 (83%) had abnormal overall EEG impression of which 77 had unfavorable outcome and 80 had epilepsy. Abnormal EEG background (particularly suppression) during neonatal period may be predictive of Unfavorable outcome. Overall impression of EEG may be predictive of clinical outcome, even when individual parameters were not predictive. Other findings did not appear to be predictive.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".