P.090 Symptomatic neonatal seizure treatment duration and seizure recurrence: a retrospective single center study
Bibliographic record
Abstract
Background: While seizures have adverse neurological effects, the prescribed antiseizure medications (ASMs) may also have a negative impact on neonatal brains and contribute to detrimental neurodevelopmental outcomes. The objectives were to evaluate: 1) the impact of implementing a neonatal seizure treatment protocol in 2016; 2) the influence of ASM duration and other clinical factors on seizure recurrence and epilepsy onset. Methods: Retrospective chart review of 139 term newborns born between 2013 and March 2021 admitted at Sainte-Justine University Center Hospital with acute symptomatic seizures. Associations were assessed using Student T-test and Fisher exact test. Results: We did not observe significant change in the number of ASMs prescribed for acute seizure control (33% required 33 ASMs before vs 22% after 2016) nor significant change in frequency of prescription of ASM at discharge over time. ASM continuation at discharge was not associated with seizure recurrence (p=0.14, OR 2.14, 95%CI 0.78-5.86) or epilepsy (p=0.78, OR 1.32, 95% CI 0.45-3.90). Epilepsy occurred in 15 (12%) of children between 15 days to 72 months of age. Conclusions: No association was found between ASM maintenance at discharge following acute symptomatic neonatal seizures and the occurrence of epilepsy. Discontinuation of ASMs should be considered prior to discharge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".