{"id":"W2891985485","doi":"10.2196/11826","title":"Data Visualizations to Support Health Practitioners’ Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study","year":2018,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Center for Future Technologies in Cancer Care, Boston University; Gordon and Betty Moore Foundation","keywords":"Multiple Chronic Conditions; Cancer; Medicine; Health care; Computer science; Nursing; Family medicine; Chronic disease; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001338898,0.0001403343,0.0002041424,0.0001815699,0.0004339848,0.0001382155,0.0004958414,0.0000265159,0.00004999417],"category_scores_gemma":[0.00005851451,0.0001166714,0.00001753647,0.0003245062,0.00007555034,0.0007853165,0.000229136,0.00003670365,0.000002296788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000120673,"about_ca_system_score_gemma":0.000298136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001291372,"about_ca_topic_score_gemma":0.000927163,"domain_scores_codex":[0.9986646,0.00008230353,0.0002866319,0.0004590122,0.0003140844,0.0001934226],"domain_scores_gemma":[0.9985067,0.00005295108,0.0002597207,0.0005684269,0.0004755978,0.0001366478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002333627,0.006646649,0.7981321,0.0008498487,0.0004423182,0.00000174722,0.1111839,0.0001558891,0.0003792019,0.008043096,0.06826432,0.005667599],"study_design_scores_gemma":[0.02764061,0.0455077,0.693231,0.000937661,0.000246993,0.000001578083,0.02217338,0.04099436,0.0008982818,0.00009605192,0.1663339,0.001938527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5187611,0.00002697227,0.4720881,0.0001180407,0.0001403542,0.004738939,0.004008067,0.0001105463,0.000007930536],"genre_scores_gemma":[0.9934202,0.000006073888,0.002467889,0.0001493743,0.00003154503,0.00008691438,0.003738076,0.0000173616,0.00008258555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4746591,"threshold_uncertainty_score":0.4757723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123066899693137,"score_gpt":0.4338126998777568,"score_spread":0.321506009908443,"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."}}