International practice patterns and perspectives on endovascular therapy for the treatment of cerebral venous thrombosis
Bibliographic record
Abstract
Background: Cerebral venous thrombosis (CVT) accounts for 0.5–1% of all strokes. The role of endovascular therapy (EVT) in the management of CVT remains controversial and variations in practice patterns are not well known. Aims: Here, we present a comprehensive, international characterization of practice patterns and perspectives on the use of EVT for CVT. Methods: A comprehensive 42-question survey was distributed to stroke clinicians globally from May to October 2023, asking about practice patterns and perspectives on the use of EVT for CVT. Results: The overall response rate was 31% (863 respondents of 2744 invited) across 61 countries. The majority of respondents (74%) supported the use of EVT for CVT in certain clinical situations. Key considerations for decision-making in using EVT favored clinical over radiographic/procedural factors and included worsening level of consciousness (86%) and worsening neurological deficits (76%). In the past 3 years, 56% of respondents used EVT for the treatment of CVT, with most (49.5%) involved in two to five cases. Among interventionalists, significant variability existed in the techniques used for EVT ( p < 0.001), with aspiration thrombectomy (56%) and stent retriever (51%) being the most used overall. Regionally, interventionalists from China predominantly used intra-sinus heparin (56%), while this technique was most commonly ranked as “never indicated” throughout the rest of the world (23%). Post-procedure, low molecular weight heparin was the most used anticoagulant (83%), although North American respondents favored unfractionated heparin (37%), while imaging was primarily split between magnetic resonance (71.8%) and computed tomography (65.9%) arteriography or venography. Conclusion: Our survey reveals significant heterogeneity in approaches to EVT for CVT, and provides a comprehensive characterization of indications, techniques, and long-term management used by clinicians internationally. This resource will aid in optimizing patient selection and endovascular treatments for future trials.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".