Endovascular treatment of cerebral sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia
Bibliographic record
Abstract
INTRODUCTION: There is little data on the role of endovascular treatment (EVT) of cerebral venous sinus thrombosis (CVST) due to vaccine-induced immune thrombotic thrombocytopenia (VITT). Here, we describe clinical characteristics and outcomes of CVST-VITT patients who were treated with EVT. PATIENTS AND METHODS: We report data from an international registry of patients who developed CVST within 28 days of SARS-CoV-2 vaccination, reported between 29 March 2021 and 6 March 2023. VITT was defined according to the Pavord criteria. RESULTS: EVT was performed in 18/136 (13%) patients with CVST-VITT (92% aspiration and/or stent retrieval, 8% local thrombolysis). Most common indications were extensive thrombosis and clinical or radiological deterioration. Compared to non-EVT patients, those receiving EVT had a higher median thrombus load (4.5 vs 3). Following EVT, local blood flow was improved in 83% (10/12, 95% confidence interval [CI] 54-96). One (6%) asymptomatic sinus perforation occurred. Eight (44%) patients treated with EVT also underwent decompressive surgery. Mortality was 50% (9/18, 95% CI 29-71) and 88% (8/9, 95% CI 25-66) of surviving EVT patients achieved functional independence with a modified Rankin Scale score of 0-2 at follow-up. In multivariable analysis, EVT was not associated with increased mortality (adjusted odds ratio, 0.66, 95% CI 0.16-2.58). DISCUSSION AND CONCLUSION: We describe the largest cohort of CVST-VITT patients receiving EVT. Half of the patients receiving EVT died during hospital admission, but most survivors achieved functional independence.
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 | 0.001 |
| 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".