{"id":"W4362706287","doi":"10.1093/jcag/gwad014","title":"Cost-effectiveness of Artificial Intelligence-Aided Colonoscopy for Adenoma Detection in Colon Cancer Screening","year":2023,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Medtronic (Canada); McGill University; Université de Montréal; McGill University Health Centre","funders":"Medtronic Canada","keywords":"Colonoscopy; Medicine; Adenoma; Colorectal cancer; Cost-effectiveness analysis; Cost effectiveness; Artificial intelligence; Computer science; Internal medicine; Cancer; Risk analysis (engineering)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004306675,0.0007182231,0.0006785557,0.0009731681,0.000259826,0.001244573,0.001004651,0.0008054889,0.001730791],"category_scores_gemma":[0.01934007,0.0003767474,0.001753826,0.0007994373,0.0007632803,0.0006196237,0.0007026098,0.0008672924,0.0000888284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01074637,"about_ca_system_score_gemma":0.008206669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1789407,"about_ca_topic_score_gemma":0.1083286,"domain_scores_codex":[0.9974744,0.001495392,0.00008569735,0.0001775191,0.0005028486,0.0002641248],"domain_scores_gemma":[0.9917367,0.006670273,0.0007176548,0.0001873744,0.0004871425,0.0002009756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001994789,0.0001774115,0.02453392,0.0006206228,0.000969101,0.0001095365,0.00003729807,0.939187,0.0005392116,0.003179489,0.00051831,0.02813332],"study_design_scores_gemma":[0.0008830833,0.00298626,0.06057278,0.0004423612,0.002616352,0.0004019675,0.00009955704,0.9201993,0.001701216,0.007470814,0.002519528,0.0001067952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9361024,0.01124596,0.03209033,0.002628051,0.00008100331,0.0008431394,0.00244802,0.0001310822,0.01443011],"genre_scores_gemma":[0.9961552,0.0007532309,0.002498514,0.00006478915,0.000006993772,0.00004819884,0.0002101693,0.000002875691,0.0002599341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789407,"threshold_uncertainty_score":0.3557982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05084100874383108,"score_gpt":0.328162198201798,"score_spread":0.277321189457967,"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."}}