{"id":"W2565216079","doi":"10.2196/diabetes.6662","title":"DiaFit: The Development of a Smart App for Patients with Type 2 Diabetes and Obesity","year":2016,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences","keywords":"Type 2 diabetes; Mobile apps; Obesity; Diabetes mellitus; Self-management; Medicine; Diabetes management; Management of obesity; Internet privacy; Intensive care medicine; Computer science; Weight loss; World Wide Web; Internal medicine; Endocrinology","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":[0.0004870438,0.0001288666,0.0002374929,0.00004735926,0.0007006515,0.000003259742,0.0001337097,0.00009724262,0.00005569123],"category_scores_gemma":[0.0001588146,0.00006256765,0.00002057399,0.000144688,0.0001001257,0.00005927623,0.00006822593,0.0001099974,0.00003993313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005496245,"about_ca_system_score_gemma":0.000367869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001925025,"about_ca_topic_score_gemma":0.00006449063,"domain_scores_codex":[0.9985136,0.00009621315,0.0004513804,0.0002298478,0.0001470258,0.0005619398],"domain_scores_gemma":[0.9976266,0.001322309,0.0002607685,0.0002939831,0.0003043711,0.0001919554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003518405,0.00009759058,0.9519829,0.0006076149,0.00001552414,3.819643e-9,0.0004638702,1.353735e-8,0.0001013824,0.0005987699,0.003088371,0.04300884],"study_design_scores_gemma":[0.001127139,0.0002178675,0.8290364,0.0002619103,0.00001267206,3.692836e-9,0.00006954357,0.000004825784,0.0002566487,0.0004370398,0.1684799,0.00009599756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932805,0.00016365,0.00005793375,0.001076537,0.0001279485,0.004982261,0.0000596061,0.00004684921,0.0002047125],"genre_scores_gemma":[0.9827699,0.00003113385,0.00143819,0.0006777551,0.00006980346,0.01457273,0.00002374626,0.00002285977,0.0003938706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1653916,"threshold_uncertainty_score":0.5388916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235093917382713,"score_gpt":0.3432879167124168,"score_spread":0.3209369775385897,"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."}}