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Record W2080666158 · doi:10.1080/02841850510021607

Hyperperfusion on perfusion computed tomography following revascularization for acute stroke

2005· article· en· W2080666158 on OpenAlexaff
Thanh Nguyen, Christopher Lum, James D. Eastwood, Peter K. Stys, M. Hogan, Mayank Goyal

Bibliographic record

VenueActa Radiologica · 2005
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineThrombolysisPerfusionPerfusion scanningCerebral blood flowRevascularizationStroke (engine)InfarctionAcute strokeNuclear medicineCardiologyRadiologyInternal medicineTissue plasminogen activatorMyocardial infarction

Abstract

fetched live from OpenAlex

PURPOSE: To describe the findings of hyperperfusion on perfusion computed tomography (CT) in four patients following revascularization for acute stroke. MATERIAL AND METHODS: In 2002-2003, among a series of 6 patients presenting with an acute stroke and treated with intra-arterial thrombolysis, we observed the presence of hyperperfusion in 3 patients on the follow-up CT perfusion. We included an additional patient who was treated with intravenous thrombolysis and who had hyperperfusion on the follow-up CT perfusion. We retrospectively analyzed their CT perfusion maps. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were compared between the affected territory and the normal contralateral hemisphere. RESULTS: In the four patients, the mean CBV and CBF were 3.6 +/- 2.0 ml/100 g and 39 +/- 25 ml/100 g/min in the affected territory compared to the normal side (mean CBV = 2.7 +/- 2.1 ml/100 g, mean CBF = 27 +/- 23 ml/100 g/min). There was no intracranial hemorrhage in the hyperperfused territories. At follow-up CT, some hyperperfused brain areas progressed to infarction, while others retained normal white to gray matter differentiation. CONCLUSION: CT perfusion can demonstrate hyperperfusion, which can be seen in an ischemic brain territory following recanalization.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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 designNot applicable
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

Citations17
Published2005
Admission routes1
Has abstractyes

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