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Implication of Deep Versus Cortical Ischemia on DWI-ASPECTS In Anterior Circulation Large Vessel Occlusion Ischemic Stroke (P3.083)

2015· article· en· W1435140311 on OpenAlexaboutno aff
Christopher Streib, Srikant Rangaraju, Ashutosh P. Jadhav, Tudor G. Jovin

Bibliographic record

VenueNeurology · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIschemiaIschemic strokeOcclusionStroke (engine)CardiologyBrain ischemiaInternal medicine

Abstract

fetched live from OpenAlex

Objective: The aim of this study is to assess whether deep and cortical ischemia, as calculated by the Alberta Stroke Program Early CT Score (ASPECTS), equally predict poor clinical outcome in anterior circulation large vessel occlusion (ACLVO) acute ischemic stroke. Background: ASPECTS is a commonly utilized selection criteria for endovascular therapy in ACLVO stroke, including in ongoing clinical trials. The ten-point topographical scoring system consists of 3 deep regions (caudate, lentiform nucleus, internal capsule) and 7 cortical regions (insula, M1-6), which are equally valued. Design/Methods: Patients with intracranial ICA, M1 and M2 occlusions who underwent endovascular therapy at UPMC between 2007-2014 were included in our analysis. Baseline demographics and outcomes including final infarct volume (FIV), hemorrhagic transformation, and 3-month modified Rankin Scale (mRS) scores were collected. DWI-ASPECTS was independently calculated on follow-up MRI (12-72 hours post-treatment) by two observers. The impact of ischemia in cortical and deep ASPECTS regions on poor clinical outcome (mRS 3-6) was assessed through logistic regression analysis. Results: 213 patients were included in the analysis (mean age 66.1±1.0 yrs, median NIHSS 15 [IQR 11-18], 3-month poor outcome rate=46.8[percnt]). Inter-rater reliability was good for DWI-ASPECTS regions (deep: Kappa=0.72; cortical: Kappa=0.63). Ischemia in cortical ASPECTS regions (OR 1.80, 95[percnt] CI 1.48-2.17, p<0.001) and deep ASPECTS regions (OR 1.57, 95[percnt] CI 1.15-2.14, p=0.005) were independent predictors of poor outcome. After controlling for FIV, both variables remained independent predictors of poor outcome (cortical: OR 1.61, 95[percnt] CI 1.28-2.03, p < 0.001; deep: OR 1.59, 95[percnt] CI 1.15-2.20, p=.005). Conclusions: Deep and cortical ischemic lesions defined by ASPECTS regions were independent predictors of poor outcome in ACLVO stroke. We did not observe a statistically significant difference between cortical and deep ischemia on clinical outcomes. Validation in a larger cohort is needed to confirm these findings.

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)0.004
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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