{"id":"W2945960778","doi":"10.2337/dci19-0020","title":"<i>Diabetes Care</i> Editors’ Expert Forum 2018: Managing Big Data for Diabetes Research and Care","year":2019,"lang":"en","type":"article","venue":"Diabetes Care","topic":"Diabetes Management and Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Hamilton Health Sciences","funders":"","keywords":"Medicine; Big data; Data management; Scope (computer science); Population; Health care; Medical record; Diabetes management; MEDLINE; Data quality; Data science; Research design; Randomized controlled trial; Diabetes mellitus; Family medicine; Type 2 diabetes; Data mining; Computer science; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008054968,0.0005561058,0.0009114344,0.0007754365,0.00054261,0.0004071546,0.001256031,0.0003256648,0.00007425906],"category_scores_gemma":[0.0003652075,0.0004873913,0.0002053625,0.0007343234,0.0004316811,0.0005371314,0.002278445,0.0007007102,0.0005764079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000266545,"about_ca_system_score_gemma":0.0001702436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001083436,"about_ca_topic_score_gemma":0.00002963926,"domain_scores_codex":[0.9938235,0.0001496637,0.0004906497,0.00162168,0.001408528,0.002505947],"domain_scores_gemma":[0.9947754,0.0007584245,0.0001093021,0.002600048,0.001239996,0.0005168024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003867596,0.00006945654,0.7288874,0.005735325,0.000268635,0.000004745621,0.001943855,0.000001743667,0.003632215,0.00003087399,0.168603,0.09078398],"study_design_scores_gemma":[0.00400159,0.002197602,0.01406516,0.001594817,0.0002192154,1.365493e-7,0.06293048,0.0009064682,0.01584034,0.0002239311,0.8971028,0.0009174371],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9324291,0.04142794,0.000003286912,0.003359001,0.00549122,0.004530459,0.001647573,0.000289551,0.0108219],"genre_scores_gemma":[0.9829413,0.0003563089,0.000523001,0.001032317,0.006528838,0.0006347588,0.006121885,0.0002415094,0.001620039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7284998,"threshold_uncertainty_score":0.9997578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06135502875428731,"score_gpt":0.3385570115750405,"score_spread":0.2772019828207531,"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."}}