{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03782958,0.002886342,0.003254897,0.005287019,0.007875417,0.02770889,0.008608061,0.03314029,0.02833905],"category_scores_gemma":[0.1286812,0.001390892,0.004084957,0.004051792,0.006578036,0.01471062,0.008323871,0.03949488,0.01938435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006289059,"about_ca_system_score_gemma":0.0150857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002717724,"about_ca_topic_score_gemma":0.008672324,"domain_scores_codex":[0.9722391,0.008721771,0.004148045,0.00279194,0.00995142,0.002147727],"domain_scores_gemma":[0.8297592,0.06336804,0.01207726,0.005261833,0.06000428,0.02952945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001464314,0.00001029046,0.00006829439,0.0001905245,0.000008913084,0.0000379972,0.00008374957,0.00001633766,0.00003550836,0.0005564609,0.9912406,0.007736695],"study_design_scores_gemma":[0.00002836725,0.00002186079,0.0002285114,0.001245929,0.00001952263,0.0001119547,0.0003154446,0.00009766493,0.00005538108,0.001774973,0.9960567,0.00004369913],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"commentary","genre_scores_codex":[0.00006597601,0.008193975,0.0006650302,0.3605827,0.6263772,0.00008725186,0.0001591439,0.0001520291,0.003716611],"genre_scores_gemma":[0.0007563144,0.0106865,0.001237235,0.2627205,0.7180354,0.0001670592,0.0002120308,0.0001867603,0.005998259],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.03782958,"threshold_uncertainty_score":0.2000643,"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."}}