P.029 Endovascular therapy for cerebral venous thrombosis: an international survey
Bibliographic record
Abstract
Background: Cerebral venous thrombosis (CVT) is a rare cause of stroke, with 10–15% of patients experiencing dependence or death. The role of endovascular therapy (EVT) in the management of CVT remains controversial and practice patterns are not well-known. Methods: We distributed a comprehensive 53-question survey to neurologists, neuro-interventionalists, neurosurgeons and other relevant clinicians globally from May 2023 to October 2023. The survey asked about practice patterns and perspectives on EVT for CVT and assessed opinions regarding future clinical trials. Results: The overall response rate was 31% (863 respondents from 2744 invited participants) across 61 countries. A majority (74%) supported use of EVT for certain CVT cases. Key considerations for EVT included worsening level of consciousness (86%) and other clinical deficits (76%). Mechanical thrombectomy with aspiration (22%) and stent retriever (19%) were the most utilized techniques, with regional variations. Post-procedurally, low molecular weight heparin was the predominant anticoagulant administered (40%), although North American respondents favored unfractionated heparin. Most respondents supported future trials of EVT (90%). Conclusions: Our survey reveals significant heterogeneity in approaches to EVT for CVT, highlighting the necessity for adequately powered clinical trials to guide standard-of-care practices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".