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Record W3175151306 · doi:10.1161/str.48.suppl_1.42

Abstract 42: Blood Biomarkers Refect Tissue Viability in Acute Ischemic Stroke

2017· article· en· W3175151306 on OpenAlexaboutno aff
Alejandro Bustamante, Mikel Terceño, Dolors Giralt, Cecile van Eendenburg, Elena López‐Cancio, Pere Cardona, Teresa García‐Berrocoso, David Cánovas, Moisès Garcés, Marta Rubiera, Eva Baldrich, Mar Castellanos, Joaquı́n Serena, Antoni Dávalos, Joan Montaner

Bibliographic record

VenueStroke · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeuroimagingStroke (engine)PathologicalBiomarkerObservational studyProspective cohort studyImaging biomarkerInternal medicinePerfusion scanningRadiologyCardiologyPerfusionMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Introduction: Assessment of tissue viability, usually performed by multimodal neuroimaging, allows the use of reperfusion therapies in selected patients, even out of the therapeutic time-window. However, multimodal imaging is still a scarce and expensive tool. The availability of blood biomarkers reflecting tissue viability could be a useful tool to manage reperfusion therapies. We aimed to test whether selected candidate biomarkers may reflect tissue viability in relation to Alberta Stroke Program Early CT score (ASPECTS) and multimodal imaging. Methods: The StrokeChip was a prospective, observational study, conducted at six Hospitals in Catalonia. Patients with suspected stroke were enrolled at Emergency Departments. Blood samples were obtained within the first six hours after symptoms onset to measure a 21-biomarker panel. Acute brain neuroimaging was dichotomized into normal (ASPECTS=10) or pathological (ASPECTS<10). For those patients with perfusion imaging, comparison was performed between patients with and without significant mismatch (>20%). Results: From August-2012 to December-2013, 941 out of 1308 patients were ischemic strokes. ASPECTS was obtained in admission neuroimaging in 927 patients. Among them, 25% displayed pathological neuroimaging. Levels of Apo-CIII disclosed a positive correlation with ASPECTS, while negative correlations were found for D-dimer, IL-6, GroA, NT-proBNP and IGFBP-3. In logistic regression analysis, Apo-CIII [OR=0.52(0.36-0.75)], D-dimer [OR=2.47(1.39-4.39)] and IGFBP-3 [OR=2.28(1.51-3.43)] were independently associated with ASPECTS <10, after adjustment by age, sex and NIHSS. Moreover, in 103 patients with baseline perfusion imaging (70% with mismatch >20%), Apo-CIII was an independent predictor of the presence of mismatch after adjustment by age, sex and NIHSS [OR=0.27(0.10-0.75)]. Conclusions: Assessment of tissue viability with blood biomarkers seems feasible. Apo-CIII might represent a surrogate marker for tissue viability assessment.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.294
Teacher spread0.279 · 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
Published2017
Admission routes1
Has abstractyes

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