{"id":"W3043995085","doi":"10.1177/0272989x20940999","title":"Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control","year":2020,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; University of Toronto","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Agency for Healthcare Research and Quality","keywords":"Smoothing; Visualization; Blood pressure; Data visualization; Medicine; Computer science; Data mining; Internal medicine; Computer vision","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":[0.007022587,0.0007832985,0.0005010983,0.001319109,0.0003173493,0.001983262,0.0007423384,0.0007041546,0.005841691],"category_scores_gemma":[0.06061912,0.0003474637,0.0009182132,0.0006725493,0.0002685182,0.001702845,0.001068454,0.0006905895,0.0002902212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003723069,"about_ca_system_score_gemma":0.0005001611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005521477,"about_ca_topic_score_gemma":0.001139854,"domain_scores_codex":[0.9957336,0.003112755,0.0002997475,0.0002705749,0.0004869142,0.00009649764],"domain_scores_gemma":[0.9410299,0.04870721,0.004893563,0.001866728,0.002589476,0.0009130407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007499909,0.002098834,0.09665509,0.006358365,0.001226843,0.0008576817,0.01413715,0.004135916,0.01888908,0.001182809,0.01413906,0.8328192],"study_design_scores_gemma":[0.009289015,0.02776281,0.654054,0.0111825,0.006748715,0.007272854,0.02006487,0.0793504,0.0686238,0.01974079,0.09433616,0.001574072],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482353,0.00666883,0.03181897,0.003812259,0.0002896576,0.000506984,0.0008460216,0.001768889,0.006053187],"genre_scores_gemma":[0.9257564,0.0017068,0.0709318,0.0004260751,0.000156242,0.0002347661,0.0002491003,0.00005561404,0.000483303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007022587,"threshold_uncertainty_score":0.03713948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062574727875679,"score_gpt":0.3526853218320791,"score_spread":0.2464278490445112,"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."}}