{"id":"W2897458127","doi":"10.2196/11366","title":"Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood Pressure Data","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"Agency for Healthcare Research and Quality","keywords":"Outlier; Blood pressure; Control (management); Medicine; Computer science; Data mining; Statistics; Data science; Internal medicine; Artificial intelligence; Mathematics","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":[0.01916946,0.0004049709,0.0003440653,0.001691832,0.0009674771,0.005341282,0.000595486,0.00100149,0.001961354],"category_scores_gemma":[0.1775876,0.0003913452,0.0004318628,0.0009454447,0.001474674,0.004223556,0.002383332,0.00119046,0.0001412985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111481,"about_ca_system_score_gemma":0.0009058962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001704246,"about_ca_topic_score_gemma":0.002107125,"domain_scores_codex":[0.9751586,0.02031731,0.001051262,0.0008315069,0.002365115,0.000276276],"domain_scores_gemma":[0.7357282,0.2334282,0.01858595,0.004356499,0.005898718,0.002002399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002993812,0.0004900193,0.5857453,0.001387966,0.0006178946,0.0009261381,0.1895096,0.004794385,0.007692357,0.004321686,0.004754751,0.1967662],"study_design_scores_gemma":[0.0003236864,0.002174445,0.6577075,0.001852926,0.0008980596,0.003145919,0.2200386,0.05769243,0.009787352,0.02747077,0.01814901,0.0007594179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834065,0.0006892037,0.009051489,0.002772369,0.00003873535,0.00006783856,0.0001802415,0.000166459,0.003627214],"genre_scores_gemma":[0.9951379,0.0001434278,0.004464298,0.0001092833,0.0000123201,0.00001972799,0.00003869104,0.00001574257,0.00005867254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01916946,"threshold_uncertainty_score":0.101379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2622078369381037,"score_gpt":0.525709429930391,"score_spread":0.2635015929922873,"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."}}