P.116 Cerebral Sinovenous Thrombosis in Preterm Infants
Bibliographic record
Abstract
Background: Neonatal cerebral sinovenous thrombosis (CSVT) can lead to severe brain injury and long-term neurodevelopmental impairments. Previous studies of neonatal CSVT have mainly included term infants. In this study, we examined the clinical and radiological features, treatment and outcome of CSVT in preterm infants. Methods: This was a retrospective cohort study of preterm infants born <37 weeks with radiologically confirmed CSVT. All MRI/MRV and CT/CTV scans were re-reviewed. Clinical and radiological data were analysed using descriptive statistics, ANOVA and chi-square tests. Results: A total of 26 preterm infants with CSVT were included. Of these, 65% were late preterm, 27% very preterm and 8% extreme preterm. Most were symptomatic (seizures 50%, abnormal exam 50%). Radiological features included transverse sinus (85%) and sagittal sinus thrombosis (42%), intraventricular hemorrhage (42%) and venous infarction (19%). Most preterm infants with CSVT (69%) were treated with anticoagulation. Anticoagulation was not associated with new or worsening intracranial hemorrhage. Outcome at follow-up ranged from no impairment (39%), mild impairment (19%), severe impairment (19%) and death (23%). Conclusions: Preterm infants with CSVT are often symptomatic and present with a distinct pattern of brain injury. Anticoagulation treatment of preterm CSVT appeared to be safe. Further studies and treatment guidelines for preterm CSVT are needed.
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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".