{"id":"W4317907993","doi":"10.1186/s12874-022-01829-w","title":"Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"U.S. National Library of Medicine; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Proportional hazards model; Cohort; Pooling; Survival analysis; Artificial neural network; Medicine; Nomogram; Framingham Risk Score; Disease; Statistics; Computer science; 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":["metaresearch","sts"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07599111,0.0001464966,0.0007827501,0.0001540583,0.001479511,0.000004827877,0.0007806068,0.0002690127,0.000107533],"category_scores_gemma":[0.0966256,0.00008313239,0.0003644973,0.001393651,0.001290477,0.00006222606,0.0004468242,0.001929232,0.00003070176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155708,"about_ca_system_score_gemma":0.003352694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003106469,"about_ca_topic_score_gemma":0.01001779,"domain_scores_codex":[0.9446831,0.04998937,0.001135258,0.0004522089,0.002655694,0.001084314],"domain_scores_gemma":[0.864984,0.1317859,0.0003204928,0.001023066,0.001434624,0.0004519161],"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":[0.001605721,0.00007054574,0.5436463,0.0007984273,0.0004693371,0.000002761818,0.002844412,0.4135023,0.00001150938,0.01040636,0.01002935,0.01661298],"study_design_scores_gemma":[0.0003394281,0.0002753231,0.04640828,0.0002421615,0.0002200783,3.227564e-7,0.00403111,0.9108672,0.00003439417,0.0355804,0.001920263,0.00008101523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2280747,0.0007949421,0.7614257,0.004029369,0.001253922,0.004090086,0.0001733666,0.00006879721,0.00008910667],"genre_scores_gemma":[0.9890032,0.0004134772,0.006771649,0.0001038421,0.0007563414,0.002306057,0.00008150855,0.00005159857,0.0005122823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7609285,"threshold_uncertainty_score":0.9998204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7636271820778228,"score_gpt":0.6357989451054679,"score_spread":0.1278282369723549,"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."}}