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Record W3028339403 · doi:10.1161/str.51.suppl_1.wmp61

Abstract WMP61: RNA Expression Signature to Diagnosis Stroke Etiology by Atrial Fibrillation versus Large Artery Atherosclerosis Cause: A BASE Clinical Trial Analysis

2020· article· en· W3028339403 on OpenAlexaff
Glen C. Jickling, Frank R. Sharp, Edward C. Jauch

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Internal medicineCardiologyEtiologyCoronary artery disease

Abstract

fetched live from OpenAlex

Background: Identifying atrial fibrillation in embolic stroke of ischemic stroke populations would be of significant clinical utility. Using the Biomarkers of Acute Stroke Etiology (BASE) trial (NCT02014896) dataset, our goal was to determine if blood gene expression signatures accurately differentiated patients with atrial fibrillation from large artery stroke patients. Methods: The BASE trial enrolled suspected stroke patients presenting to 20 hospitals within 24 hrs of symptom onset. Final gold standard diagnosis and stroke etiology were determined by an adjudication committee using all hospital data but blinded to RNA test results. Whole blood, obtained in PAXgene tubes, was frozen at -20C within 72 hrs and analyzed at a core lab (Ischemia Care, LLC, Dayton, OH) using Affymetrix HTA micro arrays. Approximately 38,000 genes on the HTA microarray were filtered to eliminate genes with low expression or high CV (> 10%) when run on replicate samples leaving 9,513 potential signature genes. A two-way random forest classifier was built through cross validation of the training data resulting in a 23 gene diagnostic signature. Results: There were 58 patients enrolled between 18 and 24 hours of symptom onset, with NIHSS>5, 27 (47%) with atrial fibrillation cause of stroke and 31 (53%) with large artery stroke; 64% were male, and median (IQR) age was 69.7 (62.8, 81.0). Median (IQR) time from symptoms to sample collection was 1323.5 (1208.8, 1381.3) minutes. Coexistent pathology at presentation was high blood pressure 49 (84%), hyperlipidemia 28 (48%), diabetes 9 (16%), and coronary artery disease 15 (26%). The panel was able to distinguish atrial fibrillation from large vessel stroke with a C-statistic 0.92 (0.55-1.0, 95% CI), sensitivity 0.90 (0.51-1.0, 95% CI) and specificity of 0.85. Conclusion: RNA expression differentiates strokes due to atrial fibrillation from large artery stroke and may have therapeutic and outcome implications in ischemic stroke populations.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.051
GPT teacher head0.335
Teacher spread0.284 · 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 designRandomized trial
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
Published2020
Admission routes1
Has abstractyes

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