{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003822651,0.0015018,0.001192439,0.003788579,0.0008544335,0.002220936,0.001173992,0.001522773,0.007072889],"category_scores_gemma":[0.01398908,0.0007874123,0.0008868268,0.001594504,0.0005458359,0.002466501,0.002373014,0.001102824,0.003514641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006684921,"about_ca_system_score_gemma":0.001956815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002337491,"about_ca_topic_score_gemma":0.003883109,"domain_scores_codex":[0.9969502,0.001275271,0.0002751725,0.0007918089,0.0006055883,0.0001020072],"domain_scores_gemma":[0.9851288,0.01198357,0.0007534088,0.00081986,0.0009945396,0.0003198899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001255344,0.0004134122,0.01526786,0.004705607,0.0003436527,0.003006641,0.01310231,0.008678473,0.07513986,0.01329589,0.1329534,0.7318375],"study_design_scores_gemma":[0.0004019118,0.0006149001,0.02815293,0.001376356,0.0005362819,0.006939392,0.00602159,0.3243752,0.106067,0.03915285,0.4858992,0.0004623414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06457238,0.005154235,0.8255551,0.002494374,0.0005309613,0.00146533,0.02399756,0.06614445,0.01008555],"genre_scores_gemma":[0.2044035,0.001882692,0.7530726,0.0007770539,0.000402324,0.0007065625,0.03015588,0.002717414,0.005881948],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007072889,"threshold_uncertainty_score":0.0236612,"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."}}