{"id":"W4392902244","doi":"10.46747/cfp.7003216","title":"Could artificial intelligence improve patient care and physician workload?","year":2024,"lang":"en","type":"article","venue":"Canadian Family Physician","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Surprise; Workload; Psychology; Computer science; Medical emergency; Nursing; Medicine; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001434456,0.0002145361,0.0002229424,0.0002145184,0.001146735,0.0001157371,0.000113831,0.0002129304,0.00001049927],"category_scores_gemma":[0.00002133627,0.0002131357,0.00005813541,0.0005140868,0.00007552101,0.0002121087,0.00003217963,0.0006832189,0.0004079372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006399314,"about_ca_system_score_gemma":0.002110741,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01604518,"about_ca_topic_score_gemma":0.03385918,"domain_scores_codex":[0.9980906,0.0001604078,0.0004329674,0.0004644745,0.0001711327,0.0006803956],"domain_scores_gemma":[0.9987583,0.0001120936,0.00005579269,0.0002953503,0.0002623484,0.0005160903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000621565,0.00001124382,0.00002791275,0.0002274299,0.00002046156,0.00001984163,0.006692733,0.0003681666,0.0005462978,0.0254197,0.002462356,0.9641976],"study_design_scores_gemma":[0.0002764821,0.001139348,0.001147481,0.004584557,0.0001663893,8.239549e-7,0.2241723,0.1140507,0.0008365546,0.01265033,0.6388611,0.002113939],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4481188,0.03210863,0.01142435,0.01130002,0.01422382,0.006219131,0.001823155,0.0009512576,0.4738308],"genre_scores_gemma":[0.9455879,0.0001778105,0.0009135018,0.05182849,0.0009797292,0.0001642541,0.0001283165,0.00006147099,0.0001585278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9620837,"threshold_uncertainty_score":0.9905071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05185779507100365,"score_gpt":0.35703056249347,"score_spread":0.3051727674224664,"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."}}