{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001442018,0.0002948239,0.0005972859,0.0009855791,0.0004714677,0.00161516,0.0004756251,0.001326801,0.004284095],"category_scores_gemma":[0.003687611,0.0001164126,0.0004384028,0.001642141,0.001462664,0.001208661,0.0007787176,0.002604194,0.0005497461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009319778,"about_ca_system_score_gemma":0.001591519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002484143,"about_ca_topic_score_gemma":0.002844528,"domain_scores_codex":[0.9993073,0.0003423089,0.00005671341,0.00008699875,0.0001479625,0.00005881327],"domain_scores_gemma":[0.9976038,0.001847258,0.0001884845,0.00005134303,0.0001672767,0.0001417837],"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":[0.0002787057,0.000173636,0.0102167,0.007359086,0.0003560027,0.0008860999,0.001065047,0.002328361,0.00129496,0.1837807,0.05382312,0.7384375],"study_design_scores_gemma":[0.0000556224,0.0003615845,0.02732202,0.006826581,0.0002977856,0.003560708,0.000963517,0.00591673,0.0008217434,0.4698119,0.4839634,0.0000983982],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.003909002,0.9456697,0.004496707,0.0323092,0.002506712,0.00001958136,0.00008829373,0.00004440768,0.01095643],"genre_scores_gemma":[0.07604139,0.8993438,0.004076288,0.008028662,0.007866453,0.00006303858,0.0001188858,0.0000193159,0.004442208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004284095,"threshold_uncertainty_score":0.0143317,"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."}}