{"id":"W4323350964","doi":"10.1093/jcag/gwac036.125","title":"A125 TANDEM STUDY DESIGN IS LESS LIKELY TO DEMONSTRATE IMPROVED ADENOMA DETECTION RATE THAN PARALLEL STUDY DESIGN IN THE ASSESSMENT OF ARTIFICIAL INTELLIGENCE-ASSISTED COLONOSCOPY","year":2023,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Colonoscopy; Randomized controlled trial; Medicine; MEDLINE; Systematic review; Meta-analysis; Adenoma; Internal medicine; Colorectal cancer; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1756565,0.002639219,0.01414647,0.005090029,0.001529015,0.005340593,0.002572633,0.006808812,0.009858905],"category_scores_gemma":[0.3016534,0.001550096,0.03134982,0.006383099,0.003730185,0.005114491,0.00240685,0.003197649,0.001111677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00318546,"about_ca_system_score_gemma":0.0037281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001245202,"about_ca_topic_score_gemma":0.002555973,"domain_scores_codex":[0.6726695,0.1938166,0.09179935,0.01877346,0.02169155,0.001249546],"domain_scores_gemma":[0.6050632,0.3061563,0.06455994,0.0135327,0.009453372,0.001234505],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.04520334,0.0003868659,0.02725865,0.4316407,0.4336608,0.0007462025,0.0007495884,0.0009687045,0.002087391,0.003232524,0.002779248,0.05128607],"study_design_scores_gemma":[0.06475645,0.01875977,0.05164055,0.08116891,0.7388641,0.001442222,0.0005918134,0.003922592,0.002189369,0.0154653,0.02072784,0.0004711044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08104984,0.8038089,0.04625901,0.004512095,0.010681,0.03983611,0.003515665,0.0003527014,0.009984582],"genre_scores_gemma":[0.8042832,0.05979309,0.05845734,0.008007179,0.002957571,0.06239015,0.00181274,0.0001783859,0.002120298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8243435,"threshold_uncertainty_score":0.9289714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04306039274843064,"score_gpt":0.3270081790283978,"score_spread":0.2839477862799671,"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."}}