{"id":"W3160380386","doi":"10.1161/circoutcomes.11.suppl_1.102","title":"Abstract 102: Impact of Using Older Data on the Accuracy of Cardiovascular Risk Scores","year":2018,"lang":"en","type":"article","venue":"Circulation Cardiovascular Quality and Outcomes","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bentley (Canada)","funders":"","keywords":"Medicine; Framingham Risk Score; Risk assessment; Population; Demographics; Disease; Demography; Internal medicine; Environmental health; 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":[0.006198582,0.0003549496,0.00179516,0.0002170098,0.0003186131,0.00003050383,0.0003119802,0.000232775,0.00005910303],"category_scores_gemma":[0.00297436,0.0002218535,0.004429486,0.0004400373,0.0005577978,0.0002374595,0.0001638601,0.0003513492,0.000008574673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089408,"about_ca_system_score_gemma":0.0003396666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006436745,"about_ca_topic_score_gemma":0.00001451965,"domain_scores_codex":[0.9957213,0.0007531315,0.0009950356,0.0006766118,0.001472001,0.000381876],"domain_scores_gemma":[0.9939816,0.0008242395,0.0004545099,0.004018048,0.0005011879,0.0002204302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001973762,0.0001459748,0.9138238,0.000737878,0.03045205,0.00001108609,0.000638883,0.006326722,0.0001925949,0.000181721,0.00006593845,0.04722602],"study_design_scores_gemma":[0.001909768,0.0000615401,0.9922361,0.0001850775,0.003103907,0.00003706658,0.0001965805,0.0007420592,0.0004890184,0.00008142173,0.0007201356,0.0002373312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817175,0.01244923,0.004047627,0.00008071956,0.0002512038,0.001009697,0.0002327494,0.00003962729,0.000171599],"genre_scores_gemma":[0.9978952,0.001373737,0.0001922718,0.00008537496,0.0003281439,0.000006151019,0.00007654781,0.00004009327,0.000002472575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07841234,"threshold_uncertainty_score":0.9730476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1492301699444535,"score_gpt":0.4067819097350481,"score_spread":0.2575517397905946,"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."}}