{"id":"W2645528799","doi":"10.1093/ije/dyw248","title":"Data Resource Profile: National electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)","year":2017,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Alberta; University of Calgary","funders":"","keywords":"Primary care; Medicine; Electronic medical record; Medical record; Resource (disambiguation); Medical emergency; Family medicine; Computer science; Internal medicine; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.002786629,0.001185401,0.001469866,0.0145566,0.001676632,0.002195805,0.00326579,0.0009548682,0.0603784],"category_scores_gemma":[0.02760559,0.0006793795,0.001006707,0.0306381,0.0002873536,0.001420926,0.001432166,0.00111048,0.01942685],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008881372,"about_ca_system_score_gemma":0.04876566,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9130458,"about_ca_topic_score_gemma":0.9279436,"domain_scores_codex":[0.9966033,0.0002151885,0.0008637693,0.0003644067,0.00143388,0.000519436],"domain_scores_gemma":[0.965654,0.003171959,0.003255737,0.001773667,0.02401015,0.002134661],"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":[0.0002145912,0.00007105547,0.0266376,0.001394336,0.00010681,0.00005710753,0.0001445867,0.0003608634,0.000240238,0.0007002401,0.9518309,0.01824158],"study_design_scores_gemma":[0.0004156936,0.0000570548,0.3743799,0.001523364,0.000251745,0.0002068077,0.0005910993,0.001530386,0.0009876532,0.001120795,0.6187385,0.0001969946],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005363512,0.00003514074,0.000115173,0.00008170825,0.000009220221,0.0001285081,0.997965,0.00009529541,0.001033733],"genre_scores_gemma":[0.003600202,0.0001372019,0.001514003,0.000150138,0.00001755725,0.0004427498,0.9923896,0.00006281441,0.001685776],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9911186,"threshold_uncertainty_score":0.2019859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4583917073867911,"score_gpt":0.535116700476173,"score_spread":0.07672499308938191,"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."}}