{"id":"W2795392050","doi":"10.1145/3173574.3173996","title":"More Text Please! Understanding and Supporting the Use of Visualization for Clinical Text Overview","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Visualization; Computer science; Process (computing); Data visualization; Field (mathematics); Data science; Clinical Practice; Information visualization; Information retrieval; Medicine; Data mining","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":[0.005101166,0.00109992,0.0003791148,0.001758264,0.0009492,0.003735149,0.001463225,0.001570461,0.04937094],"category_scores_gemma":[0.04339845,0.0005454823,0.0006683124,0.0009015172,0.0009751298,0.00647757,0.00322651,0.001229142,0.01079289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004684817,"about_ca_system_score_gemma":0.0007909521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005435715,"about_ca_topic_score_gemma":0.001146093,"domain_scores_codex":[0.9979731,0.001169993,0.00019699,0.0002242743,0.0003371983,0.00009845568],"domain_scores_gemma":[0.9686798,0.02319155,0.001574996,0.002661888,0.002692639,0.001199192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001514897,0.0004415253,0.008319093,0.005788564,0.00009535894,0.001973127,0.0462191,0.001994577,0.04242839,0.01251472,0.2189988,0.659712],"study_design_scores_gemma":[0.000516662,0.001623303,0.01522666,0.003127466,0.0002454094,0.001959847,0.01174898,0.01983539,0.02630054,0.02492867,0.8941174,0.0003698626],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1040822,0.002185289,0.6651649,0.02471887,0.001604437,0.004360008,0.009166079,0.1149002,0.07381803],"genre_scores_gemma":[0.3090571,0.001549953,0.6356328,0.003018498,0.0005487764,0.00276922,0.004193348,0.009123205,0.03410711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04937094,"threshold_uncertainty_score":0.1651623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4092592397933965,"score_gpt":0.4792159905290924,"score_spread":0.06995675073569585,"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."}}