Abstract TMP82: Comparison Of Direct Oral Anticoagulants To Vitamin K Antagonists For Treatment Of Cerebral Venous Sinus Thrombosis
Bibliographic record
Abstract
Introduction: Direct oral anticoagulants (DOACs) have gained popularity in treating cerebral venous thrombosis (CVT). However, studies comparing the use of DOACs to Vitamin K antagonists (VKA) among patients with CVT are limited. Methods: We conducted a single-center retrospective cohort study comparing VKA to DOAC-treated CVT patients. Clinical, radiographic findings and outcomes were compared. Continuous and categorical variables were compared using t-test or Wilcoxon test and Chi-square or Fisher's exact test, as appropriate. Results: 82 CVT patients were included in final analysis (mean age 41.3±16.3, 76.8% women). Thirty (37%) were treated with DOACs. There was no difference in clinical or radiographic characteristics between the two groups. There was no death and majority of patents were discharged home (p=0.11). Sixty-one patients (74.4%) had follow-up imaging within a year. Fifteen, thirty-seven and nine patients had complete, partial, and no vessel recanalization, respectively. There was no difference in recanalization status between the DOAC and VKA groups (p=0.53). 68 patients (82.3%) had follow-up data on headache status: 21(31%) reported resolution and 45(66%) partial improvement with no difference between DOAC and VKA groups (p=0.81). One patient in the DOAC group had a recurrent CVT. One patient in the VKA group had a major hemorrhage within 3 months. Conclusion: We found no significant difference in venous recanalization or outcomes in patients with CVT treated with DOAC vs VKA. DOAC appears to be a safe alternative to VKA. Large multicenter studies are needed to better evaluate the efficacy and safety of DOAC in CVT.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".