{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000229352,0.0001708724,0.0002655115,0.0001702708,0.0001888311,0.00005168145,0.00006910661,0.00007516043,0.0001472478],"category_scores_gemma":[0.0001396144,0.0001577663,0.00008861717,0.0004981862,0.00005200687,0.00009629125,0.00006087173,0.0003196555,0.000007356503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004805438,"about_ca_system_score_gemma":0.0003797738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001803757,"about_ca_topic_score_gemma":0.0001673977,"domain_scores_codex":[0.9986459,0.00007151527,0.0003335521,0.0003814714,0.0003446407,0.0002229014],"domain_scores_gemma":[0.9993629,0.00010473,0.00007866258,0.0001731846,0.0002116103,0.00006894248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006770213,0.001261678,0.9729237,0.0000352193,0.0002397416,0.00003717146,0.0008097029,0.0002057333,0.01166341,0.0009906095,0.00006059363,0.005002236],"study_design_scores_gemma":[0.001431607,0.004480849,0.9301794,0.0001391754,0.0002273145,0.000005615294,0.003030137,0.02741927,0.03189437,0.000947209,0.00006247739,0.0001826325],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664551,0.00005753191,0.02928474,0.0002019589,0.0008483596,0.001268779,0.000007278446,0.0001253979,0.001750878],"genre_scores_gemma":[0.9911091,0.000001836878,0.008176043,0.0001200201,0.0002087217,0.0001810796,0.00001464001,0.00001413118,0.0001744414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04274435,"threshold_uncertainty_score":0.6433525,"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."}}