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Record W4379598489 · doi:10.1161/strokeaha.122.042336

Prevalence of Venous Infarction in Patients With Cerebral Venous Thrombosis: Baseline Diffusion-Weighted MRI and Follow-Up MRI

2023· article· en· W4379598489 on OpenAlexaff
Eiman Al‐Ajmi, Jeremy Zung, Marie Duquet-Armand, Jonathan M. Coutinho, Daniel M. Mandell

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineVenous thrombosisDiffusion MRIMagnetic resonance imagingRadiologyStroke (engine)Brain infarctionCerebral infarctionInfarctionThrombosisCardiologyInternal medicineIschemiaMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: A venous pattern of infarction on neuroimaging is used as a clue to undiagnosed cerebral venous thrombosis (CVT); prevention of venous infarction is a goal of CVT management; and venous infarction is a factor used for clinical prognostication. Despite widespread use of the term venous infarct, the prevalence of true venous infarction is unclear. Our primary aim was to determine the prevalence of venous infarction in patients with CVT. We also measured the prevalence of diffusion abnormality without infarction, vasogenic edema, and intracranial hemorrhage. METHODS: Single-center, retrospective cohort study using a registry of 110 consecutive patients admitted to hospital with cerebral venous thrombosis between 2004 and 2014. Inclusion criteria were brain magnetic resonance imaging (MRI) and contrast-enhanced venography at presentation, and repeat brain MRI ≥1 month later. Exclusion criteria were dural arteriovenous fistula, arteriovenous malformation, cavernous sinus thrombosis, or previous neurosurgical procedure. Main outcome was proportion of patients with venous infarction (irreversible ischemic injury) diagnosed using diffusion-weighted MRI at presentation, confirmed using T2-weighted fluid-attenuated inversion recovery MRI ≥1 month later, and reported with 95% CI using the Wilson score interval method. We also report the prevalence of transient diffusion MRI abnormality without infarction, vasogenic edema, and intracranial hemorrhage. RESULTS: Seventy-three patients met the inclusion criteria, and after exclusions, the final study population was 59 patients with median age 41 years (interquartile range, 32-57). Venous infarction occurred in 12% (7/59 [95% CI, 6%-23%]) of patients, and final infarct volume was >1 mL in only 5.1% (3/59) of patients. An additional 8% (5/59 [95% CI, 4%-18%]) of patients had a transient diffusion MRI abnormality without infarction. Prevalence of cerebral vasogenic edema and intracranial hemorrhage were 66% (39/59 [95% CI, 53%-77%]) and 54% (32/59 [95% CI, 41%-66%]), respectively. CONCLUSIONS: In patients with CVT, venous infarction is uncommon and venous infarcts are typically very small. Vasogenic edema and hemorrhage are more common consequences of CVT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.239
Teacher spread0.229 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2023
Admission routes1
Has abstractyes

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