MétaCan
Menu
Back to cohort

Identification of Penumbra and Infarct in Acute Ischemic Stroke Using Computed Tomography Perfusion–Derived Blood Flow and Blood Volume Measurements

2006· article· en· W2134366767 on OpenAlexafffund
Blake Murphy, Allan J. Fox, D.H. Lee, Demetrios J. Sahlas, Sandra E. Black, Matthew J. Hogan, Shelagh B. Coutts, Andrew M. Demchuk, Mayank Goyal, Richard I. Aviv, Sean Symons, Irene Gulka, Vadim Beletsky, David M. Pelz, Vladimir Hachinski, Richard Chan, T.-Y. Lee

Bibliographic record

VenueStroke · 2006
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsWestern UniversityOttawa Public HealthRobarts Clinical TrialsFoothills Medical CentreHealth Sciences CentreSunnybrook Health Science CentreLawson Health Research Institute
FundersCollege of Science and HealthCanadian Stroke Network
KeywordsMedicinePenumbraComputed tomographyPerfusion scanningBlood flowStroke (engine)PerfusionAcute strokeCerebral blood flowIschemic strokeInfarctionRadiologyBlood volumeCardiologyInternal medicineIschemiaNuclear medicineMyocardial infarctionTissue plasminogen activator

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: We investigated whether computed tomography (CT) perfusion-derived cerebral blood flow (CBF) and cerebral blood volume (CBV) could be used to differentiate between penumbra and infarcted gray matter in a limited, exploratory sample of acute stroke patients. METHODS: Thirty patients underwent a noncontrast CT (NCCT), CT angiography (CTA), and CT perfusion (CTP) scan within 7 hours of stroke onset, NCCT and CTA at 24 hours, and NCCT at 5 to 7 days. Twenty-five patients met the criteria for inclusion and were subsequently divided into 2 groups: those with recanalization at 24 hours (n=16) and those without (n=9). Penumbra was operationally defined as tissue with an admission CBF <25 mL x 100 g(-1) x min(-1) that was not infarcted on the 5- to 7-day NCCT. Logistic regression was applied to differentiate between infarct and penumbra data points. RESULTS: For recanalized patients, CBF was significantly lower (P<0.05) for infarct (13.3+/-3.75 mL x 100 g(-1) x min(-1)) than penumbra (25.0+/-3.82 mL x 100 g(-1) x min(-1)). CBV in the penumbra (2.15+/-0.43 mL x 100 g(-1)) was significantly higher than contralateral (1.78+/-0.30 mL x 100 g(-1)) and infarcted tissue (1.12+/-0.37 mL x 100 g(-1)). Logistic regression using an interaction term (CBFxCBV) resulted in sensitivity, specificity, and accuracy of 97.0%, 97.2%, and 97.1%, respectively. The interaction term resulted in a significantly better (P<0.05) fit than CBF or CBV alone, suggesting that the CBV threshold for infarction varies with CBF. For patients without recanalization, CBF and CBV for infarcted regions were 15.1+/-5.67 mL x 100 g(-1) x min(-1) and 1.17+/-0.41 mL x 100 g(-1), respectively. CONCLUSIONS: We have shown in a limited sample of patients that CBF and CBV obtained from CTP can be sensitive and specific for infarction and should be investigated further in a prospective trial to assess their utility for differentiating between infarct and penumbra.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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 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

Citations293
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueStrokeSame topicAcute Ischemic Stroke ManagementFrench-language works237,207