{"id":"W2891312812","doi":"10.23889/ijpds.v3i4.1004","title":"Use of linked electronic health records to evaluate cardiovascular risk prediction models in Ontario, Canada","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health Promotion and Cardiovascular Prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute for Clinical Evaluative Sciences","funders":"","keywords":"Medicine; Cohort; Medical record; Record linkage; Framingham Risk Score; Blood pressure; Cohort study; Database; Myocardial infarction; Framingham Heart Study; Demography; Family medicine; Emergency medicine; Gerontology; Medical emergency; Internal medicine; Environmental health; Disease; Population; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009395218,0.00008884629,0.0002503504,0.0003985479,0.000203635,0.00005710778,0.000428405,0.00003733273,0.00004770687],"category_scores_gemma":[0.000899971,0.00008439741,0.0001244587,0.0003631526,0.00004478976,0.001244875,0.0001050032,0.0002665301,0.000001693],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002958272,"about_ca_system_score_gemma":0.0057773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8495478,"about_ca_topic_score_gemma":0.905615,"domain_scores_codex":[0.9967797,0.0002428816,0.0006940155,0.0003620271,0.001612718,0.0003086169],"domain_scores_gemma":[0.9977622,0.0000294207,0.0002755229,0.0005097844,0.001193786,0.0002292955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005812757,0.000321429,0.3335572,0.00007781816,0.001076099,0.00001128239,0.001167084,0.07916344,0.0002553203,0.001122521,0.006121361,0.5765452],"study_design_scores_gemma":[0.001219711,0.0003709253,0.8548902,0.0002293422,0.00006459831,0.0001273971,0.00003169788,0.1080115,0.00003579253,0.0007183391,0.03421174,0.00008881265],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7797583,0.00009148278,0.2156302,0.0009733417,0.002509979,0.0007830369,0.0002007353,0.00001189842,0.00004106499],"genre_scores_gemma":[0.9932343,0.0001704617,0.005491635,0.0002895874,0.0003073446,0.00000836804,0.0003486805,0.000009163639,0.0001404951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5764564,"threshold_uncertainty_score":0.999859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1174307786376014,"score_gpt":0.3909245163310187,"score_spread":0.2734937376934173,"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."}}