{"id":"W3210824720","doi":"10.21203/rs.3.rs-958135/v1","title":"Comparison of State-Of-The-Art Neural Network Survival Models With The Pooled Cohort Equations for Cardiovascular Disease Risk Prediction","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Proportional hazards model; Pooling; Cohort; Survival analysis; Artificial neural network; Medicine; Computer science; Statistics; Artificial intelligence; Internal medicine; Mathematics","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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009094797,0.0002906157,0.0009465246,0.0001376628,0.002322136,0.00003887222,0.0008728767,0.0003381251,0.00005060982],"category_scores_gemma":[0.002622115,0.0001872985,0.0008225422,0.0008241096,0.0005127709,0.0001139069,0.001206655,0.003893308,0.0000074818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004390278,"about_ca_system_score_gemma":0.003693952,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007474532,"about_ca_topic_score_gemma":0.008391616,"domain_scores_codex":[0.9852555,0.008933385,0.001327191,0.000760342,0.002676447,0.001047096],"domain_scores_gemma":[0.9852986,0.006018995,0.00069049,0.002494037,0.005194027,0.0003038379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003530376,0.00008303756,0.3194195,0.002144854,0.0006044573,0.00000123125,0.003032068,0.6710084,0.000001559712,0.0005627011,0.001468092,0.001321038],"study_design_scores_gemma":[0.0002334063,0.0002005946,0.05816946,0.002689502,0.000644792,1.237099e-7,0.01073393,0.9183489,0.0000399865,0.007935596,0.0007990294,0.0002046766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7584272,0.00944384,0.1976619,0.003419084,0.002572337,0.02424783,0.003835673,0.0001144789,0.0002776314],"genre_scores_gemma":[0.9938058,0.000479947,0.0002984587,0.00001926672,0.0006021041,0.003888508,0.0005509757,0.00007857243,0.0002763899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2612501,"threshold_uncertainty_score":0.9991348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.310725653595216,"score_gpt":0.5205198769652457,"score_spread":0.2097942233700297,"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."}}