Cerebral Venous Sinus Thrombosis: Current Updates in the Asian Context
Bibliographic record
Abstract
BACKGROUND: Cerebral venous sinus thrombosis (CVT) is a life-threatening cause of stroke in Asian countries. South Asia, comprising of India, Pakistan, and Bangladesh, contributed to 40% of strokes in women. Major CVT registries are from the Western nations, which differs from the Asian countries with respect to epidemiology, gender biases, and risk factors. This review focuses on the various aspects of relevance in evaluation and management of patients with CVT in the Asian context. SUMMARY: The incidence of CVT is higher in Asia than in Western nations. Young age, female gender, especially in pregnancy and puerperal period, and dehydration appear to be the critical risk factors. Tropical infections like malaria, scrub typhus, and flaviviral encephalitis predispose to CVT. There is a higher prevalence of inherited thrombophilia in the Asian cohorts, contributing to prothrombotic states. Anticoagulation and supportive management offer excellent outcomes. Newer anticoagulants are safe and efficacious. In medically refractory cases, endovascular treatment offers modest benefits. Decompressive hemicraniectomy, when done early, offers mortality benefits in patients with large hemorrhagic venous infarctions. KEY MESSAGES: CVT is an important cause of stroke with a high burden in South Asian countries. Establishment of robust registries is the need of the hour to study the natural history, course, and outcomes and to develop management algorithms tailored to the available resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".