{"id":"W2529643570","doi":"10.1038/srep34540","title":"Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Gilead Sciences","keywords":"Heart rate variability; Myocardial infarction; Medicine; Cardiology; Frequency domain; Acute coronary syndrome; Internal medicine; Machine learning; Artificial intelligence; Heart rate; Computer science; Blood pressure","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":[0.001938919,0.0001201468,0.0003719831,0.0001120966,0.0001071388,0.00002638748,0.00002966729,0.00008316522,0.0001520504],"category_scores_gemma":[0.0001177263,0.00007918788,0.0001912919,0.0001186979,0.0002202765,0.0001580043,0.00005478582,0.0001092917,0.000007264709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007323684,"about_ca_system_score_gemma":0.0001726201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004705103,"about_ca_topic_score_gemma":0.000001640805,"domain_scores_codex":[0.9983268,0.00007115656,0.0004669423,0.0005957661,0.0003195523,0.0002198206],"domain_scores_gemma":[0.998942,0.00003517657,0.0001167328,0.0006568372,0.0001070327,0.0001422339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008458216,0.0001316485,0.3386353,0.0001484939,0.0009061915,0.00397638,0.0003786948,0.00001021538,0.626357,0.00003079321,0.00005866741,0.02928213],"study_design_scores_gemma":[0.003062743,0.0009709154,0.7973146,0.000590483,0.001467329,0.01530652,0.00006309523,0.0004953212,0.03042876,0.005284735,0.1446153,0.0004001767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995514,0.001096545,0.001192648,0.0002414934,0.00125708,0.0004693907,0.00002583916,0.00005208368,0.0001509537],"genre_scores_gemma":[0.9967558,0.00003760246,0.0003877655,0.0000107913,0.00003000402,0.00003428528,0.00002057006,0.0000124204,0.002710789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5959282,"threshold_uncertainty_score":0.3229188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009094352970190019,"score_gpt":0.2090549415527296,"score_spread":0.1999605885825396,"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."}}