{"id":"W4408779489","doi":"10.14309/ctg.0000000000000839","title":"Artificial Intelligence as a Surrogate for Inspection Time to Assess Completeness in Esophagogastroduodenoscopy: A Prospective, Randomized, Noninferiority Study","year":2025,"lang":"en","type":"article","venue":"Clinical and Translational Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Esophagogastroduodenoscopy; Confidence interval; Prospective cohort study; Guideline; Randomized controlled trial; Surgery; Internal medicine; Endoscopy; Pathology","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":[0.001209418,0.0001462042,0.0007474329,0.0002145824,0.00009285545,0.00002113919,0.00005440919,0.00008885771,0.00003603073],"category_scores_gemma":[0.0003771547,0.0001332918,0.0001553449,0.0002238671,0.0002236288,0.00005155995,0.00002676891,0.0002573342,0.00001158044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002503839,"about_ca_system_score_gemma":0.00008753759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006478847,"about_ca_topic_score_gemma":0.0005753724,"domain_scores_codex":[0.9981468,0.0002845784,0.0007383185,0.0004893205,0.000124732,0.0002162394],"domain_scores_gemma":[0.9989457,0.0006160821,0.00006900401,0.0001086588,0.000161292,0.00009931618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.4654046,0.0007405719,0.5254005,0.00002301595,0.0001211001,0.000006302857,0.0001068683,0.0001486431,0.00003436788,0.0005166808,0.00000611503,0.007491207],"study_design_scores_gemma":[0.04402556,0.01230975,0.9201583,0.00009719167,0.0002158205,0.00002791309,0.00008496601,0.01469556,0.0000883145,0.008163774,0.00004281534,0.00009007799],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9021213,0.00003585881,0.09235269,0.002928386,0.00025093,0.002119574,0.00001296457,0.00005563519,0.0001226311],"genre_scores_gemma":[0.9970647,0.000005869859,0.001907846,0.0004243743,0.0001382668,0.0003762828,0.00001389448,0.000008058699,0.00006077329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4213791,"threshold_uncertainty_score":0.5435482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06365759337788306,"score_gpt":0.3998805672138077,"score_spread":0.3362229738359246,"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."}}