{"id":"W4407285008","doi":"10.1093/jcag/gwae059.037","title":"A37 OPTIMIZING ENDOSCOPIC SCHEDULING USING THE SMSA SCORE FOR ENDOSCOPIC MUCOSAL RESECTION.","year":2025,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Gastric Cancer Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Université de Sherbrooke","funders":"","keywords":"Resection; Endoscopic mucosal resection; Scheduling (production processes); Computer science; Medicine; Surgery; Operations management; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007150713,0.0003177486,0.0002811371,0.0009212769,0.00032089,0.0007043305,0.0003127482,0.0002278266,0.002361406],"category_scores_gemma":[0.00360821,0.0001249777,0.0005414521,0.0009509655,0.0001483951,0.0003141219,0.0003124671,0.0003213765,0.0004025771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361436,"about_ca_system_score_gemma":0.001952145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03696841,"about_ca_topic_score_gemma":0.06585554,"domain_scores_codex":[0.999691,0.00006045255,0.00003902879,0.00005606875,0.00009293648,0.00006059437],"domain_scores_gemma":[0.9978611,0.0003295302,0.0009856736,0.00004555939,0.0003197159,0.0004584339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001092839,0.00002230577,0.9957621,0.000009371492,0.00001315324,0.00002544854,0.00001214416,0.0001156421,0.00008107137,0.000008491439,0.000315311,0.003525813],"study_design_scores_gemma":[0.00001074889,0.000162182,0.9976512,0.00001465268,0.00002499249,0.0001332716,0.00008983882,0.001447356,0.00009030043,0.00002235574,0.0003471888,0.000005977856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996546,0.0003684648,0.0003513005,0.0001884995,0.000028361,0.00004188883,0.0009299229,0.00002306778,0.001522538],"genre_scores_gemma":[0.9984782,0.00009712836,0.000526114,0.00002621872,0.00001604986,0.00001947366,0.0005080458,0.000003488791,0.0003252845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03696841,"threshold_uncertainty_score":0.07350647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257136794938693,"score_gpt":0.2841063764228529,"score_spread":0.261535008473466,"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."}}