Anticoagulation in splanchnic and cerebral vein thrombosis: An international vignette‐based survey
Bibliographic record
Abstract
BACKGROUND: Anticoagulant treatment of splanchnic (SVT) and cerebral vein thrombosis (CVT) can be challenging due to the rarity of these conditions, the concomitantly high thrombotic and bleeding risks, and the available low-quality evidence. OBJECTIVES: To explore the current therapeutic approaches to SVT and CVT, and the rationale behind the anticoagulant treatment choice. METHODS: A cross-sectional survey was conducted (October 2018-April 2019) among members of three thrombosis and hemostasis societies. The survey consisted of four vignette cases: (i) SVT secondary to transient risk factor; (ii) cirrhotic SVT with esophageal varices; (iii) CVT secondary to transient risk factor; and (iv) unprovoked CVT with intracranial hemorrhage. RESULTS: A total of 397 physicians responded to the survey. There was wide variability in anticoagulant treatment options, starting time, and duration. Vitamin K antagonists were the commonest choice across the four vignette cases (44.2%-63.0%). The direct oral anticoagulants (DOACs) were the second commonest choice in low-bleeding-risk scenarios (27.7% in case 1, 22.9% in case 3), while parenteral anticoagulation alone was the second commonest choice in high-bleeding-risk scenarios (39.9% in case 2, 39.8% in case 4). The most frequent reasons for selecting DOACs were oral route of administration (50.6%), lack of need for laboratory monitoring (48.1%), and favorable safety profile of these drugs (43.4%). CONCLUSIONS: The results of our study showed that, despite being off-label, the DOACs were considered for the treatment of unusual-site venous thromboembolism. The wide variability among different physicians reflected the clinical difficulties and raised the need for more collaborative trials on these disorders.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".