{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001275374,0.0002078767,0.0002638702,0.00009158076,0.00008019443,0.0000286827,0.0002304499,0.00004211245,0.0002752829],"category_scores_gemma":[0.0007424441,0.0001574899,0.0002883992,0.0003261122,0.0001240223,0.0001251497,0.0001199983,0.0003305656,0.0000621857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001333366,"about_ca_system_score_gemma":0.0001523052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005622647,"about_ca_topic_score_gemma":0.00002555937,"domain_scores_codex":[0.9984109,0.0001219851,0.0004146081,0.0003863821,0.0004106236,0.0002555097],"domain_scores_gemma":[0.9989096,0.000125112,0.0002098195,0.0003445247,0.0002806106,0.0001303475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0423201,0.001105977,0.772902,0.0001305345,0.0001479186,0.00003816291,0.0004566899,0.00002773043,0.180712,0.0003081616,0.001142668,0.0007081173],"study_design_scores_gemma":[0.05443121,0.00305207,0.9244663,0.000576907,0.0002176741,0.0007441919,0.0001717445,0.002269982,0.01307948,0.00005826346,0.0007285796,0.0002036285],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803551,0.00006139345,0.01094468,0.003545533,0.0003946362,0.0014909,0.00004953575,0.0001196746,0.003038527],"genre_scores_gemma":[0.9818628,0.000003931753,0.01662272,0.0005230724,0.0004598398,0.0002637953,0.00003442294,0.00003288684,0.0001964828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1676325,"threshold_uncertainty_score":0.6422251,"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."}}