{"id":"W1999724776","doi":"10.1016/j.jcjd.2014.10.006","title":"Evaluating the Performance of the Framingham Diabetes Risk Scoring Model in Canadian Electronic Medical Records","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada","keywords":"Medicine; Framingham Risk Score; Diabetes mellitus; Receiver operating characteristic; Medical record; Sample (material); Population; Health records; Demography; Internal medicine; Environmental health; Health care; Disease; Endocrinology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.02156278,0.001279157,0.00121813,0.00331034,0.0016499,0.003414202,0.002474648,0.00141454,0.00157938],"category_scores_gemma":[0.1142066,0.0005316318,0.001516044,0.004972401,0.0006441103,0.001569686,0.001422542,0.001019056,0.0004971315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01135567,"about_ca_system_score_gemma":0.01499309,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7866287,"about_ca_topic_score_gemma":0.7579675,"domain_scores_codex":[0.9868233,0.004799211,0.001245419,0.001436392,0.004709828,0.0009858877],"domain_scores_gemma":[0.9403617,0.0354274,0.003801564,0.002932849,0.01589857,0.001577871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002161157,0.0002990448,0.9282796,0.0002424974,0.001239277,0.0001057836,0.0004976314,0.008700524,0.0002140064,0.0006619013,0.007219736,0.05037878],"study_design_scores_gemma":[0.0002835454,0.0004809508,0.8465939,0.0001650361,0.001339851,0.0002008288,0.0008435011,0.1457588,0.000524616,0.0006042545,0.003081398,0.0001232004],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981374,0.001973241,0.0024442,0.001534179,0.0001467549,0.0002603107,0.008067662,0.0002652933,0.003934349],"genre_scores_gemma":[0.9854318,0.0006846223,0.005958443,0.0001577347,0.00006116304,0.00008625042,0.007066031,0.00003221111,0.0005217355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2133713,"threshold_uncertainty_score":0.4292558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02692626152408662,"score_gpt":0.2717493616960397,"score_spread":0.2448231001719531,"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."}}