{"id":"W4390993463","doi":"10.1109/biocas58349.2023.10388603","title":"A Colonoscopy Training Environment with Real-Time Pressure Monitoring","year":2023,"lang":"en","type":"article","venue":"","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"","keywords":"Colonoscopy; Computer science; Training (meteorology); Real-time computing; Artificial intelligence; Medicine; Internal medicine; Colorectal 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":[0.0005485799,0.0007438585,0.0004080106,0.0003911939,0.0003031846,0.000540414,0.00114021,0.001084038,0.009821946],"category_scores_gemma":[0.001360173,0.0003068203,0.0004562844,0.0002096991,0.0002618289,0.0006433789,0.001343885,0.0006490345,0.002212405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001291215,"about_ca_system_score_gemma":0.0006678139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003036524,"about_ca_topic_score_gemma":0.0003915137,"domain_scores_codex":[0.9995754,0.00009915638,0.00002799236,0.0000895547,0.0001460575,0.0000617145],"domain_scores_gemma":[0.9989539,0.0003832905,0.00008748925,0.0001032835,0.0001525028,0.0003193626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003157822,0.005635023,0.01276662,0.001639454,0.0001100296,0.003176185,0.001622606,0.03121768,0.4965692,0.002475499,0.02190434,0.4197255],"study_design_scores_gemma":[0.001428125,0.02828568,0.05933901,0.0008533526,0.000379832,0.01161753,0.001038552,0.3179111,0.3666424,0.003699979,0.2079114,0.0008930872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3916484,0.0005985271,0.5705939,0.001584678,0.0007747678,0.002013378,0.001296733,0.01244966,0.01904005],"genre_scores_gemma":[0.6093947,0.0006324617,0.367939,0.001011567,0.0002951926,0.001715254,0.001002766,0.0003519494,0.01765716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009821946,"threshold_uncertainty_score":0.03285772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837870178274896,"score_gpt":0.2698317577887255,"score_spread":0.2414530560059766,"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."}}