Predictive Value of Clinical and EEG Features in the Diagnosis of Stroke and Hypoxic Ischemic Encephalopathy in Neonates With Seizures
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In neonates, the differentiation of stroke and hypoxic ischemic encephalopathy (HIE) is important. Neuroimaging presents technical challenges in unstable neonates, resulting in frequently delayed or missed diagnosis of stroke. Differentiating clinical and electroencephalographic (EEG) features would assist physicians in the timely diagnosis. We sought to determine, in neonates with seizures, clinical and EEG features that differentiate stroke and HIE. METHODS: Retrospective cohort study comparing clinical, seizure, and EEG features in term neonates with ischemic stroke or HIE and seizures within 7 days after birth, admitted at The Hospital for Sick Children. Putative clinical and EEG predictors of stroke were analyzed with univariate and multivariate methods. RESULTS: Sixty-two newborns with stroke (n=27) or HIE (n=35) were studied. With univariate analysis, predictors of stroke included delayed seizure onset (>or=12-hours after birth) (P<0.0001; OR, 26.4; 95% CI, 6.8, 102.5), focal motor seizures (P=0.001; OR, 7.2; 95% CI, 2.0, 26.0) and pattern of neurological abnormalities (P<0.0001). With multivariate analysis, delayed seizure onset (P<0.0001; OR 39.7; 95% CI, 7.3, 217.0) and focal motor seizures (P=0.007; OR, 13.4; 95% CI, 2.1, 87.9) predicted stroke. Presence of both predictors had 100% positive predictive value and specificity, 61% negative predictive value and 37% sensitivity. CONCLUSIONS: In neonates, onset of seizures beyond 12 hours of birth and clinically observed focal seizures are predictive of stroke. These preinvestigation indicators of stroke may facilitate earlier diagnosis and institution of specific management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".