{"id":"W2807187668","doi":"10.1016/j.gie.2018.04.2037","title":"Mo1679 REAL-TIME ARTIFICIAL INTELLIGENCE “FULL COLONOSCOPY WORKFLOW” FOR AUTOMATIC DETECTION FOLLOWED BY OPTICAL BIOPSY OF COLORECTAL POLYPS","year":2018,"lang":"en","type":"article","venue":"Gastrointestinal Endoscopy","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital","funders":"","keywords":"Colonoscopy; Medicine; Workflow; Biopsy; Artificial intelligence; General surgery; Radiology; Gastroenterology; Internal medicine; Colorectal cancer; Computer science; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004634843,0.0003194315,0.0005698993,0.0002199049,0.0001888281,0.00003802173,0.0001530296,0.0001209051,0.0001951265],"category_scores_gemma":[0.001306571,0.0003150133,0.0002364085,0.0005772027,0.0004560411,0.0001003278,0.0000528835,0.0002407848,0.00009146705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002371822,"about_ca_system_score_gemma":0.000200909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002008743,"about_ca_topic_score_gemma":0.00006839539,"domain_scores_codex":[0.9977204,0.00007497765,0.0007050048,0.0005324312,0.0003813814,0.000585841],"domain_scores_gemma":[0.9981894,0.0005493065,0.000286958,0.0002915157,0.0004028805,0.0002799237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0371651,0.0002269206,0.0005278029,0.0001038143,0.00004047961,0.0000117431,0.00007541024,0.000010541,0.9495095,0.00004627993,0.000314316,0.01196807],"study_design_scores_gemma":[0.001131788,0.05671537,0.001369855,0.0006438381,0.0002532451,0.002971794,0.0001449692,0.09070124,0.8452261,0.0004893079,0.0000419442,0.0003105611],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7308642,0.00002148317,0.2667902,0.0001502745,0.0003211873,0.000765196,0.00003399078,0.0002807075,0.0007727764],"genre_scores_gemma":[0.7576704,0.000002677959,0.2416696,0.00002208677,0.0003257448,0.0001268901,0.00002870443,0.00004286037,0.0001109983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1042834,"threshold_uncertainty_score":0.9999302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777725635948862,"score_gpt":0.2893149287567967,"score_spread":0.2715376723973081,"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."}}