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Record W2119821740 · doi:10.1148/radiol.2503080257

Hemorrhagic Transformation of Ischemic Stroke: Prediction with CT Perfusion

2009· article· en· W2119821740 on OpenAlexaff
Richard I. Aviv, Christopher D. d’Esterre, Julia Hopyan, Brian Buck, Gabriella Mallia, Vivian Li, Liying Zhang, Sean Symons, Ting‐Yim Lee

Bibliographic record

VenueRadiology · 2009
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsLawson Health Research InstituteGrey Nuns Community HospitalWestern UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineOdds ratioConfidence intervalPerfusion scanningPerfusionStroke (engine)Magnetic resonance imagingInternal medicineLogistic regressionTissue plasminogen activatorCerebral blood flowProspective cohort studyNuclear medicineReceiver operating characteristicCardiologyRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether admission computed tomography (CT) perfusion-derived permeability-surface area product (PS) maps differ between patients with hemorrhagic acute stroke and those with nonhemorrhagic acute stroke. MATERIALS AND METHODS: This prospective study was institutional review board approved, and all participants gave written informed consent. Forty-one patients who presented with acute stroke within 3 hours after stroke symptom onset underwent two-phase CT perfusion imaging, which enabled PS measurement. Patients were assigned to groups according to whether they had hemorrhage transformation (HT) at follow-up magnetic resonance (MR) imaging and CT and/or whether they received tissue plasminogen activator (TPA) treatment. Clinical, demographic, and CT perfusion variables were compared between the HT and non-HT patient groups. Associations between PS and HT were tested at univariate and multivariate logistic regression analyses and receiver operating characteristic (ROC) analysis. RESULTS: HT developed in 23 (56%) patients. Patients with HT had higher National Institutes of Health Stroke Scale (NIHSS) scores (P = .005), poorer outcomes (P = .001), and a higher likelihood of having received TPA (P = .005) compared with patients without HT. Baseline blood flow (P = .17) and blood volume (P = .11) defects and extent of flow reduction (P = .27) were comparable between the two groups. The mean PS for the HT group, 0.49 mL x min(-1) x (100 g)(-1), was significantly higher than that for the non-HT group, 0.09 mL x min(-1) x (100 g)(-1) (P < .0001). PS (odds ratio, 3.5; 95% confidence interval [CI]: 1.69, 7.06; P = .0007) and size of hypoattenuating area at nonenhanced admission CT (odds ratio, 0.4; 95% CI: 0.2, 0.7; P = .002) were the only independent variables associated with HT at stepwise multivariate analysis. The mean area under the ROC curve was 0.918 (95% CI: 0.828, 1.00). The PS threshold of 0.23 mL x min(-1) x (100 g)(-1) had 77% sensitivity and 94% specificity for detection of HT. CONCLUSION: Admission PS measurement appears promising for distinguishing patients with acute stroke who are likely from those who are not likely to develop HT. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/250/3/867/DC1.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.327

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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 designBench or experimental
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

Citations165
Published2009
Admission routes1
Has abstractyes

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