{"id":"W4413962880","doi":"10.1055/a-2695-1832","title":"Colorectal mucosal exposure area assessment using artificial intelligence: a multicenter prospective observational study","year":2025,"lang":"en","type":"article","venue":"Endoscopy","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Wuhan University; National Natural Science Foundation of China","keywords":"Medicine; Colonoscopy; Observational study; Prospective cohort study; Internal medicine; Single Center; Gastroenterology; Adenoma; 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":[0.002573034,0.0003085351,0.000444869,0.0006611232,0.0003551048,0.0007009649,0.0004128229,0.0005519315,0.000704065],"category_scores_gemma":[0.006313583,0.0002691864,0.0007163767,0.0009422374,0.0002602174,0.0005096401,0.0005635959,0.0005189201,0.0001618248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002703299,"about_ca_system_score_gemma":0.0003418354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001530273,"about_ca_topic_score_gemma":0.001358547,"domain_scores_codex":[0.9979666,0.0009319052,0.0002871928,0.000370402,0.0003022748,0.0001415907],"domain_scores_gemma":[0.9942436,0.001130185,0.002931974,0.0005993242,0.0005573316,0.0005376336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002087589,0.00006189498,0.9984388,0.00002022199,0.00009716798,0.00003106368,0.00003838282,0.00003033663,0.00009163166,0.00000982358,0.00005420315,0.0009178992],"study_design_scores_gemma":[0.00004287291,0.000673035,0.9977117,0.00002062819,0.0001392115,0.0002615903,0.0001838854,0.0006223922,0.00007651281,0.00002429946,0.0002364288,0.000007499968],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988691,0.0003694002,0.0003154262,0.00004714162,0.000005088513,0.00002703444,0.0002028409,0.000003569899,0.0001603414],"genre_scores_gemma":[0.999328,0.00008649674,0.0002431649,0.00003019596,0.00001053479,0.00002004743,0.0002567692,0.000001267015,0.0000236191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002573034,"threshold_uncertainty_score":0.01360762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0895155881255571,"score_gpt":0.3934932118361282,"score_spread":0.3039776237105711,"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."}}