{"id":"W4401060233","doi":"10.1016/j.hlc.2024.06.016","title":"Optimising Cardiovascular Outcome Prediction: An Advanced AI-Enhanced CAC-DAD Score","year":2024,"lang":"en","type":"article","venue":"Heart Lung and Circulation","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Western University; McMaster University; University of Alberta","funders":"","keywords":"Medicine; Outcome (game theory); Internal medicine; Cardiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001216157,0.001003797,0.001006576,0.001577614,0.0002359828,0.002228961,0.0006485667,0.0008457055,0.002227968],"category_scores_gemma":[0.004082887,0.000243881,0.0005550885,0.0006972718,0.0001932823,0.0007554733,0.0009283747,0.001058245,0.0008674771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002861316,"about_ca_system_score_gemma":0.0004924994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001707776,"about_ca_topic_score_gemma":0.00223689,"domain_scores_codex":[0.999559,0.0001434827,0.00003887528,0.00009006146,0.0001152896,0.00005325309],"domain_scores_gemma":[0.9989791,0.0003179378,0.0001538974,0.00005974353,0.0003048534,0.0001844978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002376413,0.0007458137,0.6152406,0.0003493575,0.0007377857,0.0008128292,0.0001032595,0.03300629,0.01712667,0.002827049,0.01061927,0.3160546],"study_design_scores_gemma":[0.0004265506,0.002386671,0.3779863,0.0002803621,0.00144985,0.002826889,0.0002559181,0.5768752,0.01126488,0.01173123,0.01426365,0.000252534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8251047,0.007238967,0.1383296,0.003240747,0.00063804,0.0003309642,0.004566583,0.001789064,0.01876132],"genre_scores_gemma":[0.9587285,0.0007650639,0.03618186,0.0002641581,0.0003474286,0.00007122694,0.001506606,0.0000683997,0.002066673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002228961,"threshold_uncertainty_score":0.007453263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02078335279289248,"score_gpt":0.3080681992515005,"score_spread":0.2872848464586081,"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."}}