{"id":"W4392015008","doi":"10.1161/hypertensionaha.124.19468","title":"AI, Machine Learning, and ChatGPT in Hypertension","year":2024,"lang":"en","type":"article","venue":"Hypertension","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"Disease; Medicine; Artificial intelligence; Intensive care medicine; Machine learning; Blood pressure; Precision medicine; Computer science; Risk analysis (engineering); Data science; Pathology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002453752,0.0002402453,0.0006306607,0.0003301986,0.0001196287,0.0000436426,0.00003592551,0.0001698431,0.00008054669],"category_scores_gemma":[0.000108106,0.0001703375,0.00009041244,0.0003219106,0.00008302159,0.0001220348,0.0001277919,0.0006069561,0.0001334605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001220364,"about_ca_system_score_gemma":0.00003869161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001848007,"about_ca_topic_score_gemma":0.0000424047,"domain_scores_codex":[0.9985465,0.00005566011,0.0002814441,0.0005224739,0.0002824875,0.0003113672],"domain_scores_gemma":[0.9993782,0.000138437,0.00002233796,0.0001962043,0.0001236549,0.000141186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001190737,0.001077056,0.07769023,0.001836533,0.0009724247,0.02143331,0.001885739,0.00001207868,0.3762569,0.001658408,0.2971947,0.2187919],"study_design_scores_gemma":[0.001987853,0.0004690942,0.02190573,0.001190249,0.0007176124,0.005281347,0.0001902797,0.02467523,0.001828176,0.0002168731,0.9411761,0.0003615121],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6344432,0.3222594,0.00003509593,0.03605155,0.0009032299,0.0006259741,0.000006879919,0.0006878765,0.004986788],"genre_scores_gemma":[0.9800107,0.006770073,0.0002512541,0.01034176,0.0001888133,0.00001185785,0.00002439101,0.00004934626,0.00235174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6439813,"threshold_uncertainty_score":0.6946163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675530033947611,"score_gpt":0.2695132012600254,"score_spread":0.2427579009205493,"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."}}