{"id":"W2333751236","doi":"10.1016/s0735-1097(16)31834-4","title":"SYSTEMATIC REVIEW AND META-ANALYSIS EVALUATING THE DIAGNOSTIC ACCURACY OF CARDIAC MAGNETIC RESONANCE IMAGING TO ASSESS LEFT ATRIAL APPENDAGE THROMBI","year":2016,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Cardiac tumors and thrombi","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Magnetic resonance imaging; Appendage; Cardiac magnetic resonance; Radiology; Cardiac magnetic resonance imaging; Modality (human–computer interaction); Cardiac imaging; Cardiology; Thrombus; Internal medicine; Artificial intelligence; Anatomy","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004367888,0.0002220443,0.006016963,0.0001777437,0.00007658682,0.000006960021,0.0004088054,0.00002715243,0.00003615768],"category_scores_gemma":[0.01143734,0.00009162807,0.003835807,0.0007648251,0.0005283916,0.00005193331,0.0002113078,0.0001989709,0.000001648165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007110215,"about_ca_system_score_gemma":0.0002343647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002568235,"about_ca_topic_score_gemma":0.000002471106,"domain_scores_codex":[0.995248,0.002218057,0.001406306,0.0002229628,0.0006549773,0.0002497163],"domain_scores_gemma":[0.9899637,0.006235592,0.002171552,0.0008715156,0.0006434532,0.0001142656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.002310314,0.0001840716,0.3000144,0.05681097,0.5692156,0.0007100026,0.0007567781,0.0008431262,0.03832087,0.001135924,0.02549886,0.004199135],"study_design_scores_gemma":[0.0007717735,0.001284307,0.1361272,0.003349386,0.8549819,0.001902337,0.0004797741,0.0000150733,0.0002977771,0.0001410989,0.0004450784,0.0002042487],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.528627,0.4446545,0.0003423061,0.02174379,0.0005218507,0.003248528,0.0005208798,0.000009168429,0.0003320735],"genre_scores_gemma":[0.9929047,0.00575854,0.0003180862,0.0006805717,0.0001735086,0.000008426517,3.405325e-7,0.00002002328,0.0001357994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4642777,"threshold_uncertainty_score":0.9968898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04057405371723371,"score_gpt":0.3530449810594012,"score_spread":0.3124709273421675,"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."}}