{"id":"W2985034358","doi":"10.1101/19010785","title":"Polygenic risk scores predict diabetic complications and their response to therapy","year":2019,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Université de Montréal","funders":"Servier; Québec Consortium for Drug Discovery; Canadian Institutes of Health Research; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Medicine; Diabetes mellitus; Logistic regression; Internal medicine; Type 2 diabetes; Blood pressure; Proportional hazards model; Endocrinology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004707099,0.0008249615,0.001168601,0.0008939143,0.0002111345,0.001221057,0.0004754766,0.0007023521,0.002428992],"category_scores_gemma":[0.01221889,0.0003109447,0.003478323,0.001254892,0.0002496787,0.0005340987,0.0007361521,0.001094062,0.0002881767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002292902,"about_ca_system_score_gemma":0.0003208726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793637,"about_ca_topic_score_gemma":0.001314351,"domain_scores_codex":[0.9969522,0.001986752,0.0002211065,0.0004677013,0.0002580814,0.000114138],"domain_scores_gemma":[0.9901538,0.006023631,0.001985754,0.001164629,0.0003633581,0.0003088306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003651235,0.0001453122,0.9552872,0.0001623808,0.01154581,0.0001316835,0.00003619842,0.009453041,0.0008575171,0.0002444392,0.000961514,0.01752368],"study_design_scores_gemma":[0.0004379597,0.001735258,0.8751072,0.0001065593,0.01471811,0.0004113113,0.00008834985,0.1016481,0.001021869,0.003561667,0.001089133,0.0000744941],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743339,0.006957253,0.01303012,0.0009333221,0.0001337231,0.00006190372,0.002880341,0.000184761,0.001484735],"genre_scores_gemma":[0.9977368,0.0002675421,0.00112564,0.00006207661,0.00004866282,0.00001481202,0.0005689075,0.00001112134,0.0001644594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004707099,"threshold_uncertainty_score":0.02489382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775893798536117,"score_gpt":0.2718729069047987,"score_spread":0.2541139689194375,"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."}}