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Record W4362569395 · doi:10.1177/11795476231165750

Snakebite Induced Cerebral Venous Sinus Thrombosis: A Case Report

2023· article· en· W4362569395 on OpenAlexaff
Muhammad Yousaf, Qaisar Ali Khan, Michelle Anthony, Aliena Badshah, Parsa Abdi, Christopher Farkouh, Faiza Amatul Hadi, Rukhsar Jan, Arooba Khan, Sumaira Iram

Bibliographic record

VenueClinical Medicine Insights Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineCerebral venous sinus thrombosisHeadachesSurgeryThrombosisVenous thrombosisDisseminated intravascular coagulation

Abstract

fetched live from OpenAlex

Introduction: Cerebral venous sinus thrombosis (CVST) is a rare but highly fatal neurological condition mostly caused by prothrombotic conditions like antiphospholipid syndrome, factor V Leiden, and G20210A prothrombin polymorphism. Snake bites are a rare cause of cerebral venous sinus thrombosis that must be recognized and treated promptly to improve survival. Case presentation: We present a case of a 25-year-old male who developed headaches and seizures following a Viper snake bite. The diagnosis was made based on a magnetic resonance venogram (MRV) showing transverse sinus thrombosis with sigmoid sinus stenosis. Initially, the patient was treated with antivenom and supportive treatment for disseminated intravascular coagulation (DIC). After the diagnosis of CVST, the patient was treated with rivaroxaban and levetiracetam. The patient improved within 1 week of treatment and was advised to follow up in 3 months. Conclusion: A high index of suspicion for cerebral venous sinus thrombosis is required if the patient presents with headaches, seizures, or abnormal vision following a snake bite. Early diagnosis and management can prevent further neurological damage.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.119
GPT teacher head0.400
Teacher spread0.281 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations5
Published2023
Admission routes1
Has abstractyes

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