{"id":"W2805408893","doi":"10.1016/j.gie.2018.04.2163","title":"Tu1103 NEW SIMULATION MODELS FOR ASSESSMENT OF COLORECTAL POLYPECTOMY SKILLS","year":2018,"lang":"en","type":"article","venue":"Gastrointestinal Endoscopy","topic":"Gastric Cancer Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"Polypectomy; Medicine; Cronbach's alpha; Colonoscopy; Intraclass correlation; Reliability (semiconductor); Likert scale; Medical physics; Forceps; General surgery; Surgery; Colorectal cancer; Psychometrics; Internal medicine; Statistics; Cancer; Clinical psychology","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":[0.0001640252,0.0001655803,0.0003266903,0.0001503992,0.0000515876,0.00001490662,0.00009545693,0.000023807,0.0002100248],"category_scores_gemma":[0.0002112637,0.0001497413,0.0001394534,0.0002020749,0.00009079849,0.0001120441,0.00004543519,0.00008291872,0.000008335312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001069393,"about_ca_system_score_gemma":0.0002297754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009208838,"about_ca_topic_score_gemma":0.00001468345,"domain_scores_codex":[0.9988201,0.00001704021,0.0003279209,0.0002526072,0.0002718125,0.000310571],"domain_scores_gemma":[0.9990122,0.000253021,0.0001704489,0.0001959212,0.0002227544,0.0001456564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007012352,0.001309429,0.7884762,0.0007550394,0.0003921036,0.00005007035,0.0005553245,0.0297126,0.1213809,0.01229939,0.0199188,0.01813778],"study_design_scores_gemma":[0.01629199,0.01298508,0.3671777,0.0009202909,0.0006096216,0.0004312624,0.0001949911,0.5897968,0.005833837,0.003641352,0.001707048,0.0004100371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2076697,0.00001570344,0.7838068,0.0004278253,0.0001949282,0.0008883647,0.00001506582,0.00008678233,0.006894797],"genre_scores_gemma":[0.6600297,0.000001223744,0.3384819,0.0001083028,0.0002884794,0.00001485452,0.00002175869,0.00001745523,0.001036334],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5600842,"threshold_uncertainty_score":0.6106273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03169374746805534,"score_gpt":0.3469766345112245,"score_spread":0.3152828870431692,"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."}}