{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000891249,0.0007716432,0.0007438184,0.000693579,0.0004206404,0.001170116,0.001853234,0.001662904,0.01294907],"category_scores_gemma":[0.006803899,0.0005088777,0.001093371,0.0006660952,0.0003231517,0.0006497112,0.0008350386,0.001330798,0.00109511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426897,"about_ca_system_score_gemma":0.002461926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05590837,"about_ca_topic_score_gemma":0.034211,"domain_scores_codex":[0.9995298,0.000207887,0.00002449675,0.00006570954,0.00009991094,0.00007200997],"domain_scores_gemma":[0.9963347,0.002514951,0.0001833134,0.0001497503,0.0005932399,0.0002240913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009347963,0.00008622619,0.001513743,0.00003038845,0.00002581347,0.00002613145,0.00002143693,0.988545,0.0001476474,0.002025863,0.001663712,0.005820554],"study_design_scores_gemma":[0.00002106832,0.00002009341,0.0001767622,0.000006129271,0.00000684964,0.000007675045,0.000005484637,0.9982355,0.00009469796,0.0007687308,0.0006523453,0.000004650509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4178705,0.000751188,0.4810195,0.002461858,0.0007010569,0.0004943566,0.01109658,0.004799197,0.08080585],"genre_scores_gemma":[0.9321198,0.0002730234,0.05174855,0.0002613015,0.00007820505,0.0005479332,0.003385357,0.0003084656,0.01127734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05590837,"threshold_uncertainty_score":0.1111659,"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."}}