{"id":"W4318540695","doi":"10.21203/rs.3.rs-2510930/v1","title":"Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Center for Advancing Translational Sciences; National Institute of Nursing Research; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Risk stratification; Myocardial infarction; Stratification (seeds); Cardiology; Internal medicine; Occlusion; Medicine; Artificial intelligence; Computer science","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":[0.00408902,0.0001576192,0.0004096908,0.0003113081,0.0007410214,0.00004935025,0.0001696492,0.0003565376,0.00008894884],"category_scores_gemma":[0.007167605,0.0001083313,0.0002536439,0.0003060316,0.0001495147,0.00002621624,0.0006476099,0.001604069,0.00001193437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002136708,"about_ca_system_score_gemma":0.0002017726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005870861,"about_ca_topic_score_gemma":0.001000532,"domain_scores_codex":[0.9970261,0.0004234026,0.0003729528,0.000447923,0.001438067,0.0002915822],"domain_scores_gemma":[0.9948045,0.003833036,0.0001835843,0.0004587485,0.0005314514,0.0001886569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002973556,0.00008196353,0.961423,0.001377243,0.0005597271,0.000009713034,0.0005317586,0.0007956706,0.0001322562,0.00001815086,0.001170819,0.03360238],"study_design_scores_gemma":[0.001231089,0.0007993597,0.7555575,0.002454524,0.000657945,0.000007171781,0.0008477553,0.2294409,0.0007188551,0.0003035532,0.007799963,0.0001813702],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754204,0.005769061,0.001780801,0.01373278,0.000477023,0.002336444,0.0002439506,0.000132311,0.0001072757],"genre_scores_gemma":[0.9411294,0.05652598,0.0000997753,0.000005705168,0.0006856601,0.0004393955,0.0004859791,0.00003515379,0.0005929173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2286452,"threshold_uncertainty_score":0.8875026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0827943251926154,"score_gpt":0.4138053506985834,"score_spread":0.3310110255059679,"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."}}