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Record W2969732888 · doi:10.1161/strokeaha.119.025809

Clinical and Imaging Parameters Associated With Hyperacute Infarction Growth in Large Vessel Occlusion Stroke

2019· article· en· W2969732888 on OpenAlexaboutno aff
Daniel Puhr‐Westerheide, Steffen Tiedt, Lukas T. Rotkopf, Moriz Herzberg, Paul Reidler, Matthias P. Fabritius, Philipp M. Kazmierczak, Lars Kellert, Katharina Feil, Kolja M. Thierfelder, Franziska Dorn, Thomas Liebig, Frank A. Wollenweber, Wolfgang G. Kunz

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)CardiologyOcclusionInternal medicineInfarctionIschemic strokeRadiologyMyocardial infarctionIschemia

Abstract

fetched live from OpenAlex

Background and Purpose— Large vessel occlusion stroke leads to highly variable hyperacute infarction growth. Our aim was to identify clinical and imaging parameters associated with hyperacute infarction growth in patients with an large vessel occlusion stroke of the anterior circulation. Methods— Seven hundred twenty-two consecutive patients with acute stroke were prospectively included in our monocentric stroke registry between 2009 and 2017. We selected all patients with a large vessel occlusion stroke of the anterior circulation, documented times from symptom onset, and CT perfusion on admission for our analysis (N=178). Ischemic core volume was determined with CT perfusion using automated thresholds. Hyperacute infarction growth was defined as ischemic core volume divided by times from symptom onset, assuming linear progression during times from symptom onset to imaging on admission. For collateral assessment, the regional leptomeningeal collateral score (rLMC) was used. Clinical data included the National Institutes of Health Stroke Scale score on admission and cardiovascular risk factors. Regression analysis was performed to adjust for confounders. Results— Median ischemic core volume was 34.4 mL, and median hyperacute infarction growth was 0.27 mL/min. In regression analysis including age, sex, National Institutes of Health Stroke Scale, clot burden score, diabetes mellitus, smoking, hypercholesteremia, hypertension, Alberta Stroke Program Early CT Score, and rLMC scores, only the rLMC score had a significant, independent association with hyperacute infarction growth (adjusted β=−0.35; P <0.001). Trichotomizing patients by rLMC scores yielded 65 patients with good (rLMC >15), 67 with intermediate (rLMC 11–15) and 46 with poor collaterals (rLMC <11) with an infarction growth of 0.17 mL/min, 0.26 mL/min, and 0.41 mL/min, respectively. Conclusions— Hyperacute infarction growth strongly depends on collaterals. In primary stroke centers, hyperacute infarction growth may be extrapolated to estimate the stroke progression during transfer times to thrombectomy centers and to support decisions on which patients to transfer.

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 score0.008
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.273
Teacher spread0.263 · 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

Citations48
Published2019
Admission routes1
Has abstractyes

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