<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>9(1)</volume><submitter>Clares de Andrade JB</submitter><pubmed_abstract>&lt;h4>Objectives&lt;/h4>Identifying patients at high risk for atrial fibrillation (AF) after cryptogenic stroke remains a challenge, particularly in settings with limited access to long-term cardiac monitoring. The AFibrisk platform, a free digital decision-support tool, integrates 19 validated AF prediction scores to support post-stroke triage. We aimed to assess the concordance of AFibrisk-supported classification decisions with expert electrophysiologist consensus and compare performance across evaluator groups with different levels of clinical experience.&lt;h4>Materials and methods&lt;/h4>A prospective, cross-sectional concordance study was conducted using 29 standardized clinical vignettes. Evaluators-3 vascular neurologists, 4 cardiology residents, and 11 neurology residents-classified each ca</pubmed_abstract><journal>JAMIA open</journal><pagination>ooag001</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12924629</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A preliminary evaluation of AFibrisk: digital decision-support platform for atrial fibrillation risk assessment after cryptogenic stroke-a cross-sectional concordance study.</pubmed_title><pmcid>PMC12924629</pmcid><pubmed_authors>Clares de Andrade JB</pubmed_authors><pubmed_authors>Gomes RP</pubmed_authors><pubmed_authors>Mendes GNN</pubmed_authors><pubmed_authors>Fagundes TP</pubmed_authors><pubmed_authors>Robles AC</pubmed_authors></additional><is_claimable>false</is_claimable><name>A preliminary evaluation of AFibrisk: digital decision-support platform for atrial fibrillation risk assessment after cryptogenic stroke-a cross-sectional concordance study.</name><description>&lt;h4>Objectives&lt;/h4>Identifying patients at high risk for atrial fibrillation (AF) after cryptogenic stroke remains a challenge, particularly in settings with limited access to long-term cardiac monitoring. The AFibrisk platform, a free digital decision-support tool, integrates 19 validated AF prediction scores to support post-stroke triage. We aimed to assess the concordance of AFibrisk-supported classification decisions with expert electrophysiologist consensus and compare performance across evaluator groups with different levels of clinical experience.&lt;h4>Materials and methods&lt;/h4>A prospective, cross-sectional concordance study was conducted using 29 standardized clinical vignettes. Evaluators-3 vascular neurologists, 4 cardiology residents, and 11 neurology residents-classified each ca</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Feb</publication><modification>2026-07-16T17:46:43.209Z</modification><creation>2026-07-09T11:10:12.987Z</creation></dates><accession>S-EPMC12924629</accession><cross_references><pubmed>41727413</pubmed><doi>10.1093/jamiaopen/ooag001</doi></cross_references></HashMap>