{"id":"W4404965869","doi":"10.51731/cjht.2024.1036","title":"Artificial Intelligence–Assisted Colonoscopy for Detecting Polyps, Adenomas, Precancerous Lesions, and Colorectal Cancer","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Colonoscopy; Medicine; Colorectal cancer; Randomized controlled trial; Gold standard (test); MEDLINE; Cost effectiveness; Cancer; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005594638,0.0001495854,0.0003980839,0.0007208524,0.0003691612,0.0000859381,0.000109048,0.0001985383,0.00001712129],"category_scores_gemma":[0.0005943628,0.000130216,0.0000947752,0.0005137128,0.0001967756,0.00009596975,0.00001519328,0.0005283208,0.000001009257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002269,"about_ca_system_score_gemma":0.004224935,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008252673,"about_ca_topic_score_gemma":0.0298399,"domain_scores_codex":[0.9986136,0.00003323432,0.0005701071,0.0002268495,0.000124989,0.000431204],"domain_scores_gemma":[0.9992006,0.0001740714,0.0002095136,0.00009782621,0.0001685907,0.0001494169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006837262,0.000006625565,0.0008242045,0.0002844359,0.00006240598,0.00005107979,0.0003006763,0.00001098343,0.000484063,0.0001746346,0.000321866,0.9967953],"study_design_scores_gemma":[0.002373965,0.1044957,0.0754972,0.0212171,0.001255503,0.01689694,0.04845323,0.01710854,0.3224546,0.03097548,0.3567892,0.002482565],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7143334,0.2557865,0.006215404,0.02103921,0.001597532,0.0006683649,0.00005321286,0.0002734929,0.00003284379],"genre_scores_gemma":[0.9964397,0.001192197,0.002025932,0.0001226095,0.00009764347,0.00006675726,0.000001890056,0.00002429623,0.00002898147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9943128,"threshold_uncertainty_score":0.9983515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641731189824856,"score_gpt":0.3514271536074059,"score_spread":0.2872540346249203,"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."}}