{"id":"W2790140452","doi":"10.1093/jcag/gwy009.208","title":"A208 OPTIMIZING THE UTILITY OF CT ENTEROGRAPHY FOR THE EVALUATION OF OBSCURE GASTROINTESTINAL BLEEDING: A NOVEL HIGHLY SENSITIVE CLINICAL PREDICTION TOOL","year":2018,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Gastrointestinal Bleeding Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Medicine; Capsule endoscopy; Obscure gastrointestinal bleeding; Logistic regression; Internal medicine; Univariate analysis; Colonoscopy; Bleed; Radiology; Multivariate analysis; Colorectal cancer; Surgery; Cancer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001368375,0.0006063323,0.0005132197,0.002066659,0.0002603001,0.001531663,0.0003536947,0.0006017115,0.0006641352],"category_scores_gemma":[0.006989348,0.0002426818,0.0004916047,0.0009451719,0.0003088052,0.0009351675,0.0005316928,0.0004343708,0.0003543837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004426493,"about_ca_system_score_gemma":0.0007025529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00155266,"about_ca_topic_score_gemma":0.002574369,"domain_scores_codex":[0.9990869,0.0003102194,0.0001584462,0.0001270195,0.0002478805,0.0000696025],"domain_scores_gemma":[0.9970167,0.001266968,0.0008568773,0.00008605974,0.0005914295,0.0001820237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004017209,0.0001124303,0.9635182,0.00007502527,0.00006830502,0.0002425539,0.00004941885,0.0006725971,0.002028452,0.00008524021,0.0005123547,0.03223374],"study_design_scores_gemma":[0.00008923595,0.001102344,0.9515966,0.00008484362,0.0002301657,0.003648294,0.0002421194,0.03618456,0.004346699,0.0003489994,0.002068934,0.00005729998],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988059,0.002591351,0.006009993,0.0005088004,0.00005603425,0.0001062686,0.0005323858,0.00008512742,0.002051041],"genre_scores_gemma":[0.9926217,0.0005193644,0.006259794,0.00006374552,0.00006122277,0.00002883843,0.0002533315,0.00000351685,0.0001886181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002066659,"threshold_uncertainty_score":0.007236719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05864116135089062,"score_gpt":0.3223997683113242,"score_spread":0.2637586069604335,"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."}}