{"id":"W4281685962","doi":"10.1055/s-0042-1742496","title":"North American Medical Informatics (NAMI)","year":2022,"lang":"en","type":"article","venue":"Yearbook of Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Health care; Health informatics; Health Administration Informatics; Informatics; Biomedicine; Public health informatics; Subject matter; Medicine; Public health; Nursing; Medical education; Health policy; Knowledge management; Political science; Computer science; HRHIS; Bioinformatics","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":[0.002742126,0.0006026398,0.0005155595,0.004415776,0.00264138,0.006178764,0.0013297,0.002052194,0.1663998],"category_scores_gemma":[0.007272025,0.0003776674,0.0002863049,0.005504263,0.0009667192,0.004470797,0.004162543,0.002303667,0.0808624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002765307,"about_ca_system_score_gemma":0.01048957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003889624,"about_ca_topic_score_gemma":0.009194976,"domain_scores_codex":[0.9979373,0.0003797694,0.0001823101,0.0002374681,0.001064942,0.0001982219],"domain_scores_gemma":[0.9929202,0.00123339,0.0004771193,0.0007170622,0.002226176,0.002425989],"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":[0.000009051305,0.00002021366,0.0003843067,0.0002173007,0.00000221786,0.00004411782,0.0001647471,0.00002457816,0.0001503339,0.01371682,0.8195432,0.1657232],"study_design_scores_gemma":[6.894375e-7,0.000002500381,0.0002978457,0.00006059637,4.363598e-7,0.00004183704,0.00002473685,0.00001431946,0.00002437556,0.0005587944,0.9989721,0.000001833564],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001345998,0.03527511,0.003963544,0.02753179,0.01824203,0.0001898661,0.002833937,0.001856391,0.9087613],"genre_scores_gemma":[0.01206205,0.03834547,0.01433687,0.01158734,0.007506905,0.0004223732,0.005107644,0.0005488572,0.9100825],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1663998,"threshold_uncertainty_score":0.5566628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03767512015789204,"score_gpt":0.4136282637179435,"score_spread":0.3759531435600514,"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."}}