{"id":"W2589058495","doi":"10.22374/1710-6222.24.1.1","title":"Use of continuous exposure variables when examining dose-dependent pharmacological effects – Application to the association between exposure to higher statin doses and the incidence of diabetes","year":2017,"lang":"en","type":"article","venue":"Journal of Population Therapeutics and Clinical Pharmacology","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Université Laval; Centre Hospitalier de l’Université de Montréal; Programs for Assessment of Technology in Health Research Institute","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Diabetes mellitus; Statin; Internal medicine; Cumulative incidence; Incidence (geometry); Akaike information criterion; Logistic regression; Type 2 diabetes; Cohort; Endocrinology; Statistics; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1296362,0.001263263,0.001858988,0.004680851,0.001141721,0.002408396,0.003468393,0.002371739,0.004381758],"category_scores_gemma":[0.2265655,0.0005965969,0.005622633,0.006876193,0.002705583,0.001712032,0.002171051,0.003781187,0.0002948688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251278,"about_ca_system_score_gemma":0.002977379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009905805,"about_ca_topic_score_gemma":0.009703187,"domain_scores_codex":[0.7753536,0.2057643,0.005475197,0.004750714,0.007700698,0.0009555664],"domain_scores_gemma":[0.5869864,0.368314,0.01961777,0.01791962,0.005529672,0.001632466],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002346165,0.0004163294,0.9081806,0.001218632,0.01159004,0.0008990326,0.0008244246,0.01422911,0.0007888148,0.007658835,0.002821841,0.04902615],"study_design_scores_gemma":[0.000472936,0.005261554,0.7797766,0.001236221,0.004533256,0.001065755,0.001758386,0.1651807,0.001904288,0.02509613,0.01342631,0.00028792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4838444,0.009849363,0.4805841,0.006526929,0.001646383,0.002419641,0.006834819,0.0005491902,0.007745271],"genre_scores_gemma":[0.8752509,0.0007893417,0.1189565,0.001082215,0.0003296663,0.001752419,0.0009723265,0.00009344412,0.0007730034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8703638,"threshold_uncertainty_score":0.6855897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1526848471403638,"score_gpt":0.4611353063512005,"score_spread":0.3084504592108367,"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."}}