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Record W4416921650 · doi:10.1161/svi270000_464

Abstract 464: Non‐contrast CT ASPECTS versus CT Perfusion for Patients with Large Infarct Core Undergoing EVT

2025· article· en· W4416921650 on OpenAlexaboutno aff
Sobia Aamir, Shayan Shams, A. Irwin, D. Waworuntu, Mallika Mallavarapu, Judith A. Jeevarajan

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsModified Rankin ScaleLogistic regressionPopulationTriageCohortConfidence intervalPerfusion scanningStroke (engine)

Abstract

fetched live from OpenAlex

Introduction The Alberta Stroke Program Early CT Score (ASPECTS) from non‐contrast CT (NCCT) and CT perfusion (CTP) estimates have been used in recent trials to triage patients with large vessel occlusion (LVO) for endovascular therapy (EVT). Recent EVT trials demonstrated that patients with large infarct core (ASPECTS 0 to 5), can still benefit from EVT. The reliability and upper limit of CTP core volume estimates in this population remains unclear. We aimed to assess how ASPECTS and CTP core correlate with one another in a real‐world cohort of patients undergoing large‐core EVT, and which modality performs better for 90‐day outcome prediction. Methods From a multicentre, prospectively collected registry, consecutive patients were identified with pre‐treatment large infarct core, defined by NCCT ASPECTS, who underwent EVT. Included patients underwent both NCCT and CTP prior to treatment and had 90‐day modified Rankin Scale (mRS) recorded. Baseline demographic and clinical characteristics were compared for those with good versus poor primary outcome (mRS 0‐3 versus 4‐6). ASPECTS‐CTP correlation was evaluated with Spearman's coefficient. Three multivariable logistic regression models (ASPECTS‐based, CTP‐based, combined ASPECTS+CTP) evaluated predictors of poor outcome, with discrimination assessed by area under the receiver operating characteristic curve (AUROC) and DeLong test, and validated by 300 bootstrap resamples. Results Among 64 patients who underwent large‐core EVT, the median age was 64 years, 56.2% were female, and median NIHSS was 18 [IQR 15‐22]. Poor outcome patients (mRS 4‐6) were older and had larger CTP cores, while other baseline factors did not differ (see Table 1). CTP core volume was frequently underestimated but overall showed a significant negative correlation with NCCT ASPECTS (ρ = ‐0.459, p < 0.001). On multivariable regression, age and CTP core volume independently predicted poor 90‐day outcome, whereas ASPECTS, NIHSS, and IV thrombolysis did not (see Table 2). Final model performance was good, with AUROCs above 0.8, and after 300 bootstrap resamples, optimism‐corrected AUROCs remained high (0.787‐0.840), supporting good discriminative ability despite the small cohort. Conclusion In a multicentre registry cohort of patients who underwent EVT with large presenting infarct core, defined by ASPECTS 0‐5, the ASPECT score showed a significant negative correlation with CT perfusion‐derived core infarct volume. Multivariable regression revealed no significant difference in outcome discrimination between ASPECTS, CTP, or their combination when predicting 90‐day mRS adjusted for confounders. Age and CTP core volume consistently emerged as independent predictors of poor outcome. image

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0020.000

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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