{"id":"W3196624767","doi":"10.5334/gh.890","title":"Developing Non-Laboratory Cardiovascular Risk Assessment Charts and Validating Laboratory and Non-Laboratory-Based Models","year":2021,"lang":"en","type":"article","venue":"Global Heart","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Saskatchewan","funders":"Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences; Isfahan University of Medical Sciences; Generalitat de Catalunya; European Commission; British Heart Foundation; Arak University of Medical Sciences; Agència per a la Competitivitat de l’Empresa; National Institute for Health and Care Research","keywords":"Medicine; Risk assessment; Chart; Population; Proportional hazards model; Cohort; Framingham Risk Score; Statistics; Predictive modelling; Disease; Environmental health; Internal medicine; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03284951,0.001186518,0.001020687,0.002204071,0.0005427561,0.002198707,0.001875112,0.0005805499,0.00147134],"category_scores_gemma":[0.08860877,0.0004830882,0.001287502,0.00130723,0.0004646526,0.001525764,0.001406116,0.001337372,0.0005206064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001772712,"about_ca_system_score_gemma":0.005373669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01570109,"about_ca_topic_score_gemma":0.01116021,"domain_scores_codex":[0.9873841,0.006799373,0.001224584,0.0009386066,0.003352859,0.0003004574],"domain_scores_gemma":[0.9149733,0.04080665,0.007721183,0.007664572,0.02794169,0.0008925176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009037601,0.001119782,0.3787031,0.0007182821,0.0008456339,0.0003873166,0.0007384369,0.2856576,0.003424738,0.007143093,0.01630571,0.3040526],"study_design_scores_gemma":[0.0002349573,0.0005874026,0.07612409,0.0003521985,0.0002365971,0.0002134285,0.0002365069,0.9078795,0.00482405,0.003824973,0.005380534,0.000105805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3071902,0.0008499987,0.6771029,0.0008858782,0.0003045151,0.002808169,0.002649104,0.004357107,0.003852123],"genre_scores_gemma":[0.5487315,0.0005222629,0.4423861,0.0001653862,0.00008879015,0.001403571,0.005798009,0.000196303,0.0007080268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03284951,"threshold_uncertainty_score":0.1737269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01394107085194991,"score_gpt":0.2692997183096548,"score_spread":0.2553586474577049,"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."}}