{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001741993,0.0000768238,0.0003846302,0.0005402641,0.00006719519,0.000006441916,0.00009954452,0.0001357224,0.00001160259],"category_scores_gemma":[0.0008480034,0.00006844669,0.0001777209,0.0004452612,0.00004203733,0.00005123218,0.00001367934,0.0002451664,4.189998e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0016073,"about_ca_system_score_gemma":0.0003940683,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01003039,"about_ca_topic_score_gemma":0.5494605,"domain_scores_codex":[0.9986399,0.0002960174,0.0005152731,0.00009358233,0.0002083234,0.0002468945],"domain_scores_gemma":[0.9982083,0.0002904144,0.0008522057,0.00007465169,0.0004802432,0.00009422489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.06080954,0.00008181115,0.8821584,0.000189507,0.0004363352,0.00001030004,0.0004916038,0.01119696,0.02562737,0.00003239146,0.0003755092,0.01859025],"study_design_scores_gemma":[0.002058941,0.009917155,0.9314195,0.0004014762,0.000184946,0.00003901946,0.0002790448,0.005774711,0.04915724,0.0004037445,0.0003036052,0.00006065436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937738,0.00001285159,0.002745964,0.002043624,0.0008466701,0.0005188399,0.000040765,0.00000524364,0.00001231191],"genre_scores_gemma":[0.9995277,0.00001219794,0.0001627191,0.00009301505,0.0001182182,0.00004050139,0.000003365107,0.00001003129,0.00003223453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5394301,"threshold_uncertainty_score":0.9965619,"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."}}