Prevalence and Characteristics of Intracranial Hemorrhages in Neonates with Hypoxic Ischemic Encephalopathy
Bibliographic record
Abstract
INTRODUCTION: The risk factors of intracranial hemorrhages (ICH) in the context of neonatal hypoxic ischemic encephalopathy (HIE) and related interventions are unclear. OBJECTIVE: This article examines the prevalence and risk factors associated with ICH in neonates with HIE. STUDY DESIGN: This is a retrospective cohort study of neonates with HIE in Southern Alberta. ICH (subdural [SDH], subarachnoid [SAH], intraventricular [IVH], intraparenchymal [IPH]) were diagnosed by magnetic resonance imaging (MRI). Perinatal and neonatal characteristics were examined. Relation of hemorrhages with hypoxic changes on MRI and HIE stages were assessed. RESULTS: = 157; brain MRI was done in 138 infants; median gestation, 40 weeks; and cooled = 103 (66%). Prevalence of SDH, IPH, IVH, and SAH were 47, 22, 11, and 10 (34.1%, 15.9%, 7.8%, 7.2%), respectively. There was no significant increase in hemorrhage with mode of delivery, seizures, hypo/hypercarbia, severe thrombocytopenia, or deranged coagulation. All hemorrhages increased with higher HIE stage, regardless of the HIE severity in MRI. Adjusting for HIE staging, cooling, and gestation, IPH was observed more in infants who received inotropes (odds ratio [OR], 3.32; 95% confidence interval [CI], 1.20, 9.20). CONCLUSION: SDH followed by IPH were the most common ICH. Thrombocytopenia and deranged coagulation did not increase risk of hemorrhages in HIE. Our study was not powered to determine the impact of inotrope use on the risk of IPH.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".