{"id":"W3048971624","doi":"10.1016/j.jcjd.2020.07.006","title":"Environmental Scan on Canadian Interactive Knowledge Translation Tools to Prevent Diabetes Complications in Patients With Diabetes","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Diabetes Management and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"North York General Hospital; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Public Health Ontario; University of Toronto; Université Laval; Centre Integre de Sante et de Services Sociaux de Laval; St. Michael's Hospital","funders":"Diabetes Action Canada; Canadian Institutes of Health Research; Université Laval","keywords":"Medicine; Checklist; Context (archaeology); Knowledge translation; Diabetes mellitus; Distress; Type 2 diabetes; MEDLINE; Family medicine; Medical education; Knowledge management; Computer science; Psychology","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":[0.004682725,0.0005795832,0.0005336326,0.001683453,0.002518894,0.002909211,0.00133965,0.0009091647,0.04810219],"category_scores_gemma":[0.02855536,0.0002575336,0.001088913,0.002930385,0.0005445098,0.001254761,0.003647411,0.001217107,0.00380564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01015088,"about_ca_system_score_gemma":0.03180841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6262556,"about_ca_topic_score_gemma":0.7946814,"domain_scores_codex":[0.9951791,0.001857051,0.0003812725,0.0002193639,0.001710346,0.0006528656],"domain_scores_gemma":[0.9794118,0.01230736,0.0005568034,0.0006397416,0.005590576,0.001493703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001826175,0.0009181085,0.03429238,0.001719064,0.0001314607,0.001202068,0.007563774,0.001251902,0.0006704421,0.004270856,0.3176395,0.6285144],"study_design_scores_gemma":[0.001288703,0.0009754628,0.1494407,0.009087235,0.001116485,0.0009895418,0.01715103,0.005466223,0.003429467,0.006923275,0.8036411,0.0004906772],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2401235,0.008027155,0.01076266,0.06574542,0.002081526,0.002277996,0.02851672,0.005234987,0.63723],"genre_scores_gemma":[0.7955217,0.01181763,0.08824503,0.01653018,0.0007100103,0.002329296,0.01558362,0.0009009262,0.0683616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6262556,"threshold_uncertainty_score":0.751891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233827046173846,"score_gpt":0.2323604026166655,"score_spread":0.210022132154927,"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."}}