Cerebral Venous Sinus Thrombosis in Preterm Infants
Bibliographic record
Abstract
BACKGROUND: Neonatal cerebral venous sinus thrombosis (CVST) can lead to brain injury and neurodevelopmental impairments. Previous studies of neonatal CVST have focused on term infants, and studies of preterm infants are lacking. In this study, we examined the clinical and radiological features, treatment and outcome of CVST in preterm infants. METHODS: This was a retrospective, consecutive cohort study of preterm infants (gestational age <37 weeks) with radiologically confirmed CVST. All magnetic resonance imaging/MRV and CT/CTV scans were re-reviewed to study thrombus characteristics and pattern of brain injury. Outcome was assessed by the validated pediatric stroke outcome measure at the most recent clinic visit. RESULTS: Twenty-six preterm infants with CVST were studied. Of these, 65% were moderate-late preterm (32-37 weeks), 27% very preterm (28-32 weeks), and 8% extreme preterm (<28 weeks). Most (73%) were symptomatic at presentation with seizures or abnormal exam. Transverse (85%) and superior sagittal (42%) sinuses were common sites of thrombosis. Parenchymal brain injury was predominantly periventricular (35%) and deep white matter (31%) in location. Intraventricular hemorrhage occurred in 46%. Most infants (69%) were treated with anticoagulation. No treated infant (including eleven with pretreatment hemorrhage) had new or worsening post-treatment hemorrhage. Outcomes ranged from no deficits (50%), mild-moderate (25%), and severe (25%) impairment. CONCLUSIONS: In our sample of preterm infants with CVST, more than one-quarter were asymptomatic. White matter brain lesions predominated and one-half had neurological deficits at follow-up. Anticoagulation of preterm CVST in this small cohort appeared to be safe. Larger studies of preterm CVST are needed.
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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 | 0.004 |
| 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 | 0.000 | 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".