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Record W4406992799 · doi:10.1161/str.56.suppl_1.wp318

Abstract WP318: Cancer and Atrial Cardiopathy: A Secondary Analysis of the ARCADIA Clinical Trial

2025· article· en· W4406992799 on OpenAlexaff
Babak B. Navi, Mitchell S.V. Elkind, Cenai Zhang, David Tirschwell, Richard A. Kronmal, Jordan J. Elm, Joseph P. Broderick, David J. Gladstone, Hooman Kamel, W.T. Longstreth

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineArcadiaStroke (engine)Atrial fibrillationCancerClinical trialCardiologyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Approximately 50% of ischemic strokes in patients with cancer are classified as cryptogenic after standard evaluation. As cancer and its treatments are associated with increased risk for atrial fibrillation (AF), the pathological substrate for AF—atrial cardiopathy—could be the underlying etiology for some of these cancer-related cryptogenic strokes. However, the relationship between cancer and atrial cardiopathy is uncertain. Methods: We conducted a post-hoc cross-sectional analysis of baseline data collected from patients enrolled into the ARCADIA trial from 2018-2023 at 185 North American sites. Patients had to be age 45 years or older with a clinical diagnosis of cryptogenic ischemic stroke within the past 180 days and without AF of any duration. Enrolled patients consented to screening for atrial cardiopathy, the presence of which was required for randomization. Linear regression models were used to examine associations between a history of cancer and the three log-transformed atrial cardiopathy biomarkers: serum N-terminal pro-B-type natriuretic peptide (NT-proBNP), P-wave terminal force in ECG lead V 1 (PTFV 1 ), and left atrial diameter index (LADI) on echocardiogram. Results: Among 3,745 patients with cryptogenic stroke, 506 (13.5%) had a history of cancer. Patients with, compared to those without, a history of cancer were older (71 versus 65 years) and more often White (88% versus 74%) and non-Hispanic (94% versus 90%). Medical comorbidities were similar between groups. History of cancer was associated with higher median values of NT-proBNP (126 versus 103 pg/mL, p<0.001) and LADI (1.9 versus 1.8 cm/m 2 , p<0.001) but similar median values of PTFV 1 (3000 versus 3025, p=0.08). After adjusting for demographics and tobacco use, no association remained between history of cancer and NT-proBNP (beta-coefficient, -0.09; 95% CI, -0.20 to 0.03), PTFV 1 (beta-coefficient, -0.04; 95% CI, -0.10 to 0.02), or LADI (beta-coefficient, 0.01; 95% CI, -0.01 to 0.04). Conclusions: In a multicenter prospective cohort of patients with cryptogenic stroke, history of cancer was not independently associated with selected biomarkers for atrial cardiopathy. These data suggest that, in the absence of apparent AF, cancer and atrial cardiopathy may not be linked in patients with stroke.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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
Published2025
Admission routes1
Has abstractyes

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