Cerebral Venous Sinus Thrombosis Associated with COVID-19: A Case Series and Literature Review
Bibliographic record
Abstract
Abstract Background: Since the COVID-19 pandemic, several cases of cerebral venous sinus thrombosis (CVST) have been reported in SARS-CoV-2 infected individuals. This study provides a series of patients with CVST and SARS-CoV-2 infection.Methods: Consecutive patients with documented SARS-CoV-2 infection, as well as clinical and radiological characteristics of CVST, were reported from three teaching hospitals in the South West, North West, and the center of Iran from June to July 2020. We also searched the abstract archives until the end of August 2020 and gathered 28 reported cases. The diagnostic criteria for SARS-CoV-2 infection were determined according to SARS-CoV-2 detection in oropharyngeal or nasopharyngeal samples in clinically suspected patients. Demographics, main COVID-19 symptoms, confirmatory tests for SARS-CoV-2 infection diagnosis, the interval between the diagnosis of SARS-CoV-2 infection and CVST, clinical and radiological features of CVST, therapeutic strategies, CVST outcomes, rate of hemorrhagic transformation, and mortality rate were investigated.Results: Six patients (aged 31 to 62 years old) with confirmed CVST and SARS-CoV-2 infection were admitted to our centers. Four patients had no respiratory symptoms of SARS-CoV-2 infection. Five out of six patients developed the clinical manifestations of CVST and SARS-CoV-2 infection simultaneously. Three patients had known predisposing factors for CVST. Despite receiving CVST and SARS-CoV-2 infection treatments, four out of six patients passed away.Conclusions: The role of SARS-CoV-2 as a “cause” versus an “additive contributor” remains to be elucidated. Practitioners should be aware of the possibility of CVST in SARS-CoV-2 infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".