Clinical Presentation, Imaging and Treatment of Cerebral Venous Thrombosis (CVT)
Bibliographic record
Abstract
Cerebral venous thrombosis (CVT) is a multi-causal disorder that affects the venous aspect of the neurovascular tree and is related to certain hereditary and acquired predispositions. The incidence, clinical presentation, imaging findings and clinical outcome of CVT are therefore variable and will relate to various risk factors that are predominant in different age groups1,2,3,4. CVT can also be a part of the dynamic progression of patients with intracra-nial dural arteriovenous fistulas as well as being associated with the syndrome of pseudotu-mor cerebri. No firm treatment methods for CVT have been established. However, anticoagulation, although not completely scientifically proven, is regarded as a first line treatment4,5,6,7. On the contrary, the interventional neuroradiological procedures such as transvenous thrombolysis 8,9,10 or mechanical thrombectomy11,12 are usually reserved for patients who do not show clinical response to heparin therapy. The purpose of this article is to discuss the present knowledge of the etiology, clinical presentation, diagnosis and management of this potentially devastating disease from the perspectives of the interventional neuroradiolo-gist.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".