{"id":"W2041971539","doi":"10.1503/cmaj.140473","title":"Primary care electronic medical records: a new data source for research in Canada","year":2014,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Calgary","funders":"","keywords":"Lagging; Primary care; Medical record; Electronic medical record; Medicine; Family medicine; Medical care; Data science; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01383232,0.0005362718,0.00141373,0.01294736,0.006656356,0.00897731,0.003344305,0.001465741,0.005945921],"category_scores_gemma":[0.05657005,0.0006910895,0.0008769179,0.04814099,0.001411186,0.003006673,0.004063079,0.003088937,0.001323038],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1046428,"about_ca_system_score_gemma":0.363989,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953767,"about_ca_topic_score_gemma":0.9967855,"domain_scores_codex":[0.9702677,0.002677306,0.003139796,0.001941975,0.02017719,0.001796069],"domain_scores_gemma":[0.899512,0.008954451,0.005657263,0.00328797,0.07266793,0.009920349],"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":[0.0003040459,0.0003187669,0.2757639,0.004801821,0.0003298604,0.0008540215,0.005042087,0.0008852945,0.0008779126,0.01348723,0.3896894,0.3076457],"study_design_scores_gemma":[0.0001662088,0.0001390985,0.6004249,0.007087142,0.0003534966,0.0004185771,0.007270943,0.003283606,0.0006866454,0.002400343,0.3775348,0.000234116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1687692,0.05214752,0.0166398,0.1708734,0.00296602,0.006548565,0.5206243,0.0014097,0.06002155],"genre_scores_gemma":[0.5067011,0.06907364,0.1335353,0.03112775,0.001576741,0.003541588,0.2347698,0.0007947179,0.0188794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8953573,"threshold_uncertainty_score":0.75924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08864128401080971,"score_gpt":0.4405566498388594,"score_spread":0.3519153658280497,"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."}}