{"id":"W2990476816","doi":"10.3233/shti190510","title":"AutoScribe: Extracting Clinically Pertinent Information from Patient-Clinician Dialogues","year":2019,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Vector Institute; University of Toronto","funders":"","keywords":"Context (archaeology); Computer science; Medical information; Information retrieval; Natural language processing; Data science; Artificial intelligence; History","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007716017,0.0001331455,0.0003580759,0.0003485935,0.0001586772,0.00003500365,0.0003707039,0.0001747048,0.000002097075],"category_scores_gemma":[0.0007870016,0.0001165391,0.00002506039,0.0003578858,0.0001097325,0.00121632,0.0006206288,0.0004611045,0.00005276228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007791942,"about_ca_system_score_gemma":0.00008995561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003684349,"about_ca_topic_score_gemma":0.00001363753,"domain_scores_codex":[0.9976463,0.00004010668,0.00168235,0.0001353973,0.0001569603,0.0003388255],"domain_scores_gemma":[0.9984427,0.0003363189,0.0006387922,0.0004459082,0.00009257085,0.00004368102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001146961,0.00003329632,0.07109912,0.0003570929,0.00003243295,0.000001113769,0.04658828,0.0002005335,0.000001004116,0.0214068,0.0002698248,0.8599991],"study_design_scores_gemma":[0.002669192,0.00185646,0.01474888,0.001526118,0.00001098421,0.00002929153,0.08718945,0.7638955,0.00005263655,0.07751822,0.04963268,0.0008705488],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9152051,0.001997216,0.07044595,0.007769327,0.002183147,0.0006779546,0.000006658313,0.0004459474,0.001268744],"genre_scores_gemma":[0.8723222,0.001820617,0.1218231,0.003967686,0.00002311928,0.00002609475,0.000006005656,0.000003611873,0.000007559715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8591285,"threshold_uncertainty_score":0.4752326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0612514249517196,"score_gpt":0.3470773507067837,"score_spread":0.2858259257550642,"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."}}