{"id":"W4213382237","doi":"10.1093/jcag/gwab049.119","title":"A120 AUTOMATED BOWEL PREPARATION DETECTION WITH DEEP. CONVOLUTIONAL NEURAL NETWORKS","year":2022,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Colonoscopy; Convolutional neural network; Artificial intelligence; Computer science; Test set; Colorectal cancer; Medicine; Internal medicine; Cancer","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":[0.0005440738,0.001533574,0.0004649134,0.001153597,0.0003052078,0.0006943978,0.001158726,0.001185064,0.005172769],"category_scores_gemma":[0.001821001,0.0005378336,0.0009172222,0.0005820037,0.0002401318,0.0006826418,0.0008225744,0.0009850754,0.002705063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109852,"about_ca_system_score_gemma":0.0009504357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02034102,"about_ca_topic_score_gemma":0.02866907,"domain_scores_codex":[0.9996672,0.00003626179,0.00001939072,0.0001300203,0.00007121346,0.00007589647],"domain_scores_gemma":[0.9995648,0.0001246532,0.00006994956,0.00007365732,0.0001350172,0.00003184397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009112151,0.0004627455,0.01849365,0.0005774896,0.0003765538,0.0004356539,0.00007453323,0.1493381,0.03817687,0.0009806898,0.04036419,0.7498083],"study_design_scores_gemma":[0.00003935651,0.0001603668,0.007896971,0.00008735438,0.00006442133,0.0002659882,0.00003057742,0.9635835,0.02058911,0.001458212,0.005791033,0.00003317712],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3942846,0.008279377,0.4932064,0.001685196,0.0008160957,0.0007504218,0.02195413,0.06429443,0.01472922],"genre_scores_gemma":[0.7585126,0.001051711,0.1944669,0.0008570717,0.0001536655,0.0003768551,0.02987042,0.0005752209,0.01413556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02034102,"threshold_uncertainty_score":0.04044527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005873598362712129,"score_gpt":0.220732934363755,"score_spread":0.2148593360010429,"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."}}