{"id":"W3216874313","doi":"","title":"Personal Patient-Generated Data Visualizations for Diabetes Patients","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Computer science; Tracking (education); Data visualization; Process (computing); Health care; Patient data; Sample (material); Data science; Visualization; Internet privacy; Medicine; Psychology; Data mining","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":[0.001189411,0.0008071335,0.000548174,0.001556283,0.0003107171,0.001789826,0.0004328538,0.0009056588,0.01934176],"category_scores_gemma":[0.00767696,0.0002445297,0.0006581315,0.001062531,0.0001601865,0.0006091215,0.001043212,0.0008458577,0.002184142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003086122,"about_ca_system_score_gemma":0.0005589193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001566522,"about_ca_topic_score_gemma":0.003216688,"domain_scores_codex":[0.9995616,0.0001830837,0.00003788538,0.00007612142,0.0001066061,0.00003462162],"domain_scores_gemma":[0.9963081,0.002113065,0.0002197402,0.0004690219,0.0005521334,0.0003379843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009356422,0.0008873406,0.07367087,0.002618443,0.0005821586,0.004578377,0.005199752,0.0351359,0.02990405,0.00433283,0.2028666,0.6308672],"study_design_scores_gemma":[0.001409298,0.002120096,0.1413469,0.001931032,0.001124893,0.008436839,0.006506177,0.4754895,0.07824769,0.02857296,0.2541984,0.0006162951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5761601,0.004221992,0.2516209,0.00956988,0.002161522,0.0007380047,0.07822201,0.05326261,0.02404298],"genre_scores_gemma":[0.8529935,0.001572052,0.1234811,0.0005169871,0.000326867,0.000246074,0.01415991,0.002045564,0.004657846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01934176,"threshold_uncertainty_score":0.06470466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03276688013242737,"score_gpt":0.2762443321474944,"score_spread":0.2434774520150671,"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."}}