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Record W4403452027 · doi:10.1159/000541937

Cerebral Venous Sinus Thrombosis: Current Updates in the Asian Context

2024· review· en· W4403452027 on OpenAlexaff
Angel Miraclin T, Deepti Bal, Ivy Sebastian, Sanjith Aaron, Jeyaraj Pandian

Bibliographic record

VenueCerebrovascular Diseases Extra · 2024
Typereview
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineContext (archaeology)Stroke (engine)Intensive care medicineCerebral venous sinus thrombosisVenous thrombosisIncidence (geometry)ThrombosisPediatricsSurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.051
GPT teacher head0.342
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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