{"id":"W4200106324","doi":"10.1016/j.jcjd.2021.12.001","title":"Enhancing Diabetes Surveillance Across Alberta by Adding Laboratory and Pharmacy Data to the National Diabetes Surveillance System Methods","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Alliance for Canadian Health Outcomes Research in Diabetes; University of Calgary; Alberta Health; University of Alberta","funders":"Novo Nordisk Canada; Novo Nordisk; Alberta Health Services","keywords":"Medicine; Pharmacy; Diabetes mellitus; Environmental health; Family medicine; 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":[0.009093859,0.0005967435,0.0004118014,0.007026299,0.000714999,0.001872931,0.001556161,0.0003281419,0.001636239],"category_scores_gemma":[0.01650337,0.0003195537,0.0005483073,0.008852672,0.0002677244,0.0005945683,0.002132318,0.0005390558,0.0002333905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007683862,"about_ca_system_score_gemma":0.01612742,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8064759,"about_ca_topic_score_gemma":0.8529944,"domain_scores_codex":[0.9920521,0.003026323,0.0006213466,0.0007389729,0.002950979,0.0006102835],"domain_scores_gemma":[0.9895973,0.001998727,0.00208387,0.0008199476,0.004703267,0.0007968496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001157665,0.0001010982,0.9303665,0.0002076232,0.0001857103,0.00005943077,0.0003185712,0.002013896,0.0004120964,0.0003621498,0.005921266,0.05993589],"study_design_scores_gemma":[0.00008561098,0.00008795267,0.9852563,0.0001618879,0.0001470028,0.00007824686,0.0004020475,0.00569289,0.0003710581,0.0002201928,0.007470752,0.0000261031],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8739317,0.00364138,0.04002769,0.004615021,0.0002525647,0.003225204,0.04478162,0.001133144,0.02839179],"genre_scores_gemma":[0.8912422,0.001771981,0.07592391,0.001172184,0.0002358186,0.001225145,0.02542286,0.00005859899,0.002947209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1935241,"threshold_uncertainty_score":0.3893275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807977402477006,"score_gpt":0.317925774076986,"score_spread":0.2898460000522159,"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."}}