{"id":"W4390962451","doi":"10.48550/arxiv.2401.07796","title":"Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Université de Lyon; Hospices Civils de Lyon; Agence Nationale de la Recherche","keywords":"Risk stratification; Stratification (seeds); Medical record; Medicine; Computer science; Cardiology; Computer vision; Radiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001144991,0.0007356618,0.0004099778,0.001103581,0.0001590866,0.0007538936,0.0005774728,0.0005499995,0.00273092],"category_scores_gemma":[0.005521394,0.0002679236,0.000590943,0.0006951983,0.0002705447,0.0007871231,0.001312401,0.00102682,0.001602621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004182742,"about_ca_system_score_gemma":0.0008314794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004351041,"about_ca_topic_score_gemma":0.006185051,"domain_scores_codex":[0.9995537,0.0001242641,0.00003728126,0.0001449747,0.00008645342,0.00005316634],"domain_scores_gemma":[0.9990871,0.0004744508,0.00009512965,0.000172725,0.0001122059,0.00005843305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008108797,0.0003447172,0.09529918,0.0002595251,0.0002197656,0.0005604279,0.0003702694,0.05212705,0.01898554,0.003201383,0.01572282,0.8120983],"study_design_scores_gemma":[0.00007235893,0.0003272897,0.04437894,0.0001353414,0.0001026617,0.0007161424,0.0001789405,0.912093,0.01828263,0.0147824,0.008859444,0.00007081822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2698644,0.001594666,0.6967614,0.002495699,0.0002488786,0.0003922968,0.01163081,0.01336255,0.003649268],"genre_scores_gemma":[0.8714483,0.0005345123,0.1159622,0.000344004,0.00009032206,0.0001763553,0.009593246,0.000163839,0.001687445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004351041,"threshold_uncertainty_score":0.009135783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03357265656613651,"score_gpt":0.2137581045746307,"score_spread":0.1801854480084942,"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."}}