Pediatric Cerebral Sinus Venous Thrombosis during the First Three Years of the SARS-CoV-2 Pandemic: A Multinational Case Series
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has been associated with thrombotic complications in adults and children. Cerebral sinovenous thrombosis (CSVT) has also been described in adults with SARS-CoV-2, though rarely reported in children. OBJECTIVE: To describe cases of SARS-CoV-2-related pediatric CSVT among patients enrolled in the International Pediatric Stroke Study. METHODS: We enrolled pediatric patients (0-18 years) with a CSVT diagnosis between March 1, 2020 and March 1, 2023, positive for SARS-CoV-2. RESULTS: Nineteen cases (median age: 9 years; IQR: 3-16) from 4 countries and 12 institutions met eligibility criteria. We found thrombosis of the superficial venous system in 90%, arterial ischemic strokes in 11%, and venous infarcts in 26% of patients. All patients had an additional thrombosis risk factor, including 63% with a positive hypercoagulability evaluation and coinfection present in 58%. Eighteen patients (95%) received antithrombotics. Outcome data were reported in16/19 (84%) (median follow-up of 12 months [IQR: 5-28]) showing partial recanalization in nine, full recanalization in four, and no recanalization in three. CONCLUSION: CSVT appears to be a rare thrombotic complication of pediatric SARS-CoV-2 infection. However, all patients in our case series had additional risk factors for thrombosis, making it difficult to determine the relationship between SARS-CoV-2 infection and CSVT development. The majority of patients had anticoagulation and among 16 patients with follow-up data, 80% demonstrated CSVT resolution or improvement. Despite this, 38% had neurological deficits at follow-up. These findings are consistent with those reported in previous pediatric CSVT case series.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".