{"id":"W4282585200","doi":"10.1016/j.cjca.2022.06.007","title":"Using Big Data for Cardiovascular Health Surveillance: Insights From 10.3 Million Individuals in the CANHEART Cohort","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Women's College Hospital; University of Toronto; University Health Network; Bruyère; Ted Rogers Centre for Heart Research; Institute for Clinical Evaluative Sciences; Statistics Canada","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Cohort; Diabetes mellitus; Population; Behavioral Risk Factor Surveillance System; Risk factor; Incidence (geometry); Cohort study; Demography; Disease; Health care; Gerontology; Environmental health; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003755569,0.0004557958,0.0005356715,0.001344838,0.0004770399,0.001659048,0.0007531716,0.0009888649,0.001274473],"category_scores_gemma":[0.0154704,0.0004775503,0.001277897,0.003469187,0.0002174065,0.001031247,0.002396139,0.001906802,0.0003756923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007346261,"about_ca_system_score_gemma":0.002292233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08599783,"about_ca_topic_score_gemma":0.1417974,"domain_scores_codex":[0.9975963,0.0008998587,0.000253781,0.0003417075,0.000579057,0.0003293027],"domain_scores_gemma":[0.9907726,0.003197422,0.001716342,0.001602752,0.001556083,0.001154751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002192079,0.00004986671,0.9423919,0.0001279243,0.0007081607,0.000154546,0.0004199635,0.0005178752,0.0001877157,0.0009047456,0.04199424,0.01232389],"study_design_scores_gemma":[0.00005323719,0.00003907759,0.9758725,0.0003058906,0.000406717,0.0002391406,0.0009237202,0.001911093,0.0001647448,0.001840131,0.01819427,0.00004952172],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6921073,0.007007652,0.004888191,0.02437826,0.0006811648,0.0001719624,0.2626972,0.00015819,0.007909993],"genre_scores_gemma":[0.8529842,0.004085393,0.006654672,0.006600257,0.0006357586,0.0003479599,0.1267557,0.0001248949,0.001811273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08599783,"threshold_uncertainty_score":0.1709945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1072752825867292,"score_gpt":0.3143800514046289,"score_spread":0.2071047688178997,"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."}}