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Record W2765431929 · doi:10.1111/ene.13493

Distinct predictive role of collateral status on clinical outcome in variant stroke subtypes of acute large arterial occlusion

2017· article· en· W2765431929 on OpenAlexaboutno aff
X. Zhang, Meixia Zhang, Wenhong Ding, Shenqiang Yan, David S. Liebeskind, Min Lou

Bibliographic record

VenueEuropean Journal of Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineStroke (engine)CollateralCardiologyInternal medicineAcute strokeOcclusionOutcome (game theory)Collateral damageTissue plasminogen activator

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Clinical trials have shown that robust collateral flow has a relationship with good clinical outcome; however, different stroke subtypes were lumped together. This study explored the relationship between baseline collaterals and the onset-to-imaging time (OIT) and the correlation between pre-treatment collateral status and clinical outcome amongst different subtypes. METHOD: Prospectively collected data from consecutive acute ischaemic stroke patients with acute middle cerebral artery occlusion who received reperfusion therapy were reviewed. The regional leptomeningeal score (20 points) was based on the scoring extent of contrast opacification in the six Alberta Stroke Program Early CT Score (ASPECTS) cortical regions (M1-6), parasagittal anterior cerebral artery territory and the basal ganglia by perfusion-derived dynamic four-dimensional computed tomography angiography (4D CTA). Stroke subtype was determined by the TOAST classification criteria. A 3-months modified Rankin Scale score of 0-2 was defined as a good outcome. RESULTS: The analysis included 158 patients: 30 (19.0%) patients had large artery atherosclerotic stroke (LAA), 87 (55.1%) cardioembolic stroke (CE) and 41 (25.9%) stroke of undetermined etiology. Baseline collateral was negatively correlated with OIT (P = 0.0205) in the CE group after adjusting for female sex, smoking, hyperlipidemia, baseline National Institutes of Health Stroke Scale (NIHSS) and baseline mismatch ratio, but not in the LAA group. Baseline collateral showed a strong relationship with good clinical outcome after adjusting for recanalization, baseline NIHSS, age and female sex (odds ratio 1.120, confidence interval 1.013-1.238, P = 0.027) in all patients and in the CE group (odds ratio 3.223, confidence interval 1.212-8.570, P = 0.019), but not in the LAA patients. CONCLUSIONS: Based on 4D CTA, sustained good leptomeningeal collaterals may predict good outcome in CE but not in LAA patients. Moreover, the extent of collaterals was associated with OIT in the CE patients, which indicates prompt reperfusion therapy in this group of patients.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.319
Teacher spread0.296 · 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 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

Citations26
Published2017
Admission routes1
Has abstractyes

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