Evaluation and Management of Cerebral Venous Thrombosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Thrombosis of the dural sinus and/or cerebral veins (CVT) is a rare but potentially devastating type of stroke that tends to occur in young adults, especially women. In this article, the abbreviation CVT refers to either cerebral venous thrombosis or dural sinus thrombosis. The purpose of this review is to review the most up-to-date literature on the epidemiology, diagnosis, management, and prognosis of CVT. In addition, illustrative cases that represent the spectrum of CVT are provided. RECENT FINDINGS: CVT represents about 0.5% of all strokes and can be challenging to diagnose because headache, rather than focal neurologic symptoms, is the prominent feature. The diagnosis is confirmed with MRI and magnetic resonance venogram (MRV). The mainstay of acute management is anticoagulation, although, in the cases of severe hemorrhagic conversion of a venous infarction, endovascular mechanical thrombectomy may be potentially lifesaving. The evaluation of underlying causes from transient triggers, eg, pregnancy, oral contraceptives, or infection, versus chronic triggers, eg, cancer and thrombophilia, will often influence the duration of anticoagulation. The outcomes after CVT are generally favorable, and the risk of recurrence is low. SUMMARY: CVT is an important diagnosis to keep in mind when evaluating patients with headache in the emergency department, and it is important that it not be overlooked because it is treatable.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".