{"id":"W3090170696","doi":"10.26443/mjm.v18i1.173","title":"Artificial Intelligence – the EHR savior?","year":2020,"lang":"en","type":"article","venue":"McGill Journal of Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Reflection (computer programming); Artificial intelligence; Computer science; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01323801,0.0009562123,0.001350096,0.001098223,0.0030666,0.01179742,0.003598927,0.01387247,0.07284348],"category_scores_gemma":[0.1235012,0.0005281901,0.0007172594,0.001041081,0.006587962,0.01507503,0.005746437,0.01469461,0.0420369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003057762,"about_ca_system_score_gemma":0.005908036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002108188,"about_ca_topic_score_gemma":0.003746282,"domain_scores_codex":[0.9904552,0.004352609,0.0009216609,0.0007453882,0.002818198,0.0007068442],"domain_scores_gemma":[0.910556,0.04468755,0.004032429,0.005562478,0.02663735,0.008524222],"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":[0.00001028881,0.000007885064,0.00002104038,0.00008171462,0.000002793447,0.00002762903,0.00006919834,0.0000129041,0.00003179178,0.004070547,0.9899643,0.005700016],"study_design_scores_gemma":[0.00001502434,0.00003142092,0.0003841504,0.0006455079,0.000006872231,0.0001160045,0.0007819124,0.00007241932,0.00009404454,0.01426556,0.9835634,0.00002360056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002356452,0.003072926,0.001475068,0.7624367,0.1772344,0.00008099162,0.0003091476,0.0003227459,0.05483235],"genre_scores_gemma":[0.01575609,0.005277305,0.003140578,0.5297374,0.1767808,0.0005758533,0.0006149167,0.0004367419,0.2676803],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07284348,"threshold_uncertainty_score":0.2436857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4074839882108878,"score_gpt":0.4561644619335223,"score_spread":0.04868047372263445,"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."}}