{"id":"W2755360770","doi":"10.2196/mhealth.7703","title":"A Smartphone App for Improvement of Colonoscopy Preparation (ColoprAPP): Development and Feasibility Study","year":2017,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smartphone app; Colonoscopy; Computer science; Mobile apps; Smartphone application; Multimedia; Medicine; Human–computer interaction; World Wide Web; Colorectal cancer; 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":[0.0007442427,0.0001074232,0.000313775,0.00005730266,0.000520115,0.00002310559,0.00003825506,0.00006093238,0.000002462403],"category_scores_gemma":[0.00007278652,0.00009372732,0.00002119244,0.00004002362,0.00005966903,0.00006380172,0.00003902356,0.0000922186,3.588715e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001450228,"about_ca_system_score_gemma":0.0005134172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004457285,"about_ca_topic_score_gemma":0.001177606,"domain_scores_codex":[0.9989328,0.00002426056,0.0003591153,0.0003197582,0.0001483539,0.0002156615],"domain_scores_gemma":[0.9990302,0.00004006636,0.0003159686,0.0002727112,0.0001059914,0.0002351174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1429376,0.002823682,0.4797657,0.00861532,0.000126862,0.000003165862,0.01431628,8.834098e-7,0.001370623,0.00007401811,0.0008652817,0.3491006],"study_design_scores_gemma":[0.004773952,0.03623215,0.9548461,0.00005640939,0.00006199744,0.000004930544,0.0005499664,0.0001804799,0.002334428,0.0000473313,0.0008282521,0.00008402209],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950449,0.0002531064,0.0003145939,0.0004621482,0.0001411348,0.00362729,0.000007416141,0.00003146772,0.0001179017],"genre_scores_gemma":[0.9975066,0.00005798402,0.001612561,0.0001301433,0.00007110275,0.0005066188,0.000007427196,0.000008292629,0.00009933872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4750803,"threshold_uncertainty_score":0.4000356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021223306074057,"score_gpt":0.4432009809465807,"score_spread":0.3410786503391751,"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."}}