{"id":"W3033416648","doi":"10.1016/j.gie.2020.03.877","title":"1116 THERMAL ABLATION OF THE MUCOSAL DEFECT MARGIN AFTER ENDOSCOPIC MUCOSAL RESECTION - A PROSPECTIVE, INTERNATIONAL, MULTI-CENTER TRIAL.","year":2020,"lang":"en","type":"article","venue":"Gastrointestinal Endoscopy","topic":"Stoma care and complications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Endoscopic mucosal resection; Thermal ablation; Ablation; Colonoscopy; Margin (machine learning); Clinical trial; Adenoma; Surgery; Resection; Endoscopic submucosal dissection; Endoscopy; Internal medicine; Colorectal cancer; 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":[0.001340336,0.0006819158,0.001291337,0.0001699315,0.0002503536,0.0007901144,0.0003728008,0.0009128294,0.003556775],"category_scores_gemma":[0.00102371,0.0002584626,0.001573627,0.000389309,0.0005795717,0.0006980225,0.0002517418,0.001641013,0.000559939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002539316,"about_ca_system_score_gemma":0.000386857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003429121,"about_ca_topic_score_gemma":0.0006879658,"domain_scores_codex":[0.99935,0.0003849918,0.00003420339,0.0000977465,0.00003685389,0.00009624099],"domain_scores_gemma":[0.9994098,0.0001940607,0.0001593515,0.0000763349,0.00003005916,0.0001303941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.9611988,0.002962988,0.004923614,0.0008336016,0.002052907,0.00005663391,0.00003102395,0.0005994684,0.003523261,0.0002665679,0.0008245579,0.02272665],"study_design_scores_gemma":[0.749042,0.1979017,0.04213634,0.0003152035,0.003607271,0.0002962428,0.0001226464,0.001392874,0.00176051,0.0005184047,0.002855166,0.00005170008],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852031,0.01002283,0.0005828264,0.0003807384,0.0002581656,0.0006117745,0.0005775215,0.00002632217,0.002336808],"genre_scores_gemma":[0.9940149,0.002310306,0.0005780292,0.0004085469,0.0002701564,0.0005134055,0.0007642953,0.00001030312,0.001130062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003556775,"threshold_uncertainty_score":0.01189864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02909249789640729,"score_gpt":0.288183029819381,"score_spread":0.2590905319229737,"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."}}