{"id":"W2263916923","doi":"10.1093/ehjci/jev354","title":"EACVI/EHRA Expert Consensus Document on the role of multi-modality imaging for the evaluation of patients with atrial fibrillation","year":2016,"lang":"en","type":"article","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":336,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Care Foundation","funders":"","keywords":"Medicine; Atrial fibrillation; Cardiac imaging; Modality (human–computer interaction); Cardiology; Magnetic resonance imaging; Cardiac magnetic resonance; Internal medicine; Heart Rhythm; Radiology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006304448,0.0001573342,0.0003509446,0.00009896606,0.0002352136,0.00004392895,0.00009769382,0.00001176101,0.00003166682],"category_scores_gemma":[0.0009439198,0.00007124661,0.001075023,0.0001151292,0.00011213,0.0001189161,0.00004952971,0.00009427814,0.000006444566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026413,"about_ca_system_score_gemma":0.00006585072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001329384,"about_ca_topic_score_gemma":2.302809e-7,"domain_scores_codex":[0.9968904,0.0009852172,0.0005129141,0.000200483,0.001216241,0.0001947179],"domain_scores_gemma":[0.9977588,0.0005663406,0.0003105295,0.0004824451,0.0008214422,0.00006046395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001179571,0.00001155328,0.5953493,0.00002682333,0.002265938,0.000003961989,0.0005588804,0.006907969,0.001094746,0.0001080978,0.0005398083,0.3919534],"study_design_scores_gemma":[0.01378914,0.0001371163,0.8872796,0.0004046027,0.002149536,0.0000457419,0.0006658624,0.007773084,0.001514969,0.0001534065,0.08587364,0.0002133573],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411104,0.01443209,0.01926955,0.01899498,0.001038499,0.004130546,0.00002156527,0.00005174019,0.0009506071],"genre_scores_gemma":[0.9977724,0.00008883134,0.0006874725,0.00008697469,0.0013018,0.000001199861,0.000004236726,0.00003434247,0.00002275159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.39174,"threshold_uncertainty_score":0.2905352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07463174263463586,"score_gpt":0.3379695417829335,"score_spread":0.2633377991482977,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}