{"id":"W4283593432","doi":"10.1136/annrheumdis-2022-eular.1416","title":"OP0152 A DEEP LEARNING FRAMEWORK FOR MRI DETECTION OF ACTIVE INFLAMMATORY AND STRUCTURAL CHANGES IN THE SACROILIAC JOINT CONSISTENT WITH AXIAL SPONDYLOARTHRITIS: AN INTERNATIONAL COLLABORATIVE STUDY","year":2022,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Spondyloarthritis Studies and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicine; Axial spondyloarthritis; Ankylosing spondylitis; Receiver operating characteristic; Sacroiliac joint; Magnetic resonance imaging; Artificial intelligence; Cohort; Machine learning; Test (biology); Sacroiliitis; Physical therapy; Radiology; Pathology; Internal medicine; Computer science","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":[0.002918831,0.00164532,0.001427606,0.001154242,0.000548233,0.001105743,0.001960856,0.001919686,0.001654951],"category_scores_gemma":[0.004262481,0.0005431501,0.00189214,0.0006311301,0.0003353565,0.0007218204,0.00149744,0.001799205,0.0006329137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008927674,"about_ca_system_score_gemma":0.002480942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03631508,"about_ca_topic_score_gemma":0.03910402,"domain_scores_codex":[0.9990006,0.0003616625,0.00005783778,0.0002400045,0.0001976382,0.0001421964],"domain_scores_gemma":[0.9990491,0.000388358,0.00005369641,0.0001075659,0.0003029766,0.00009821635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00317199,0.002774639,0.04545554,0.0009356054,0.004859385,0.000574425,0.0001404823,0.177013,0.01143648,0.001616157,0.07219709,0.6798252],"study_design_scores_gemma":[0.0006437469,0.00100908,0.01228209,0.0001854079,0.001053712,0.0003054331,0.00007243734,0.9671582,0.005826196,0.002201802,0.009179124,0.00008280687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6153224,0.01845598,0.3174659,0.003151366,0.001145596,0.001305168,0.02582356,0.009504008,0.007826086],"genre_scores_gemma":[0.7869595,0.002470955,0.1508989,0.001744127,0.0002972756,0.0009774463,0.04453875,0.0006583935,0.01145464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03631508,"threshold_uncertainty_score":0.07220739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456648195308824,"score_gpt":0.3046471515574715,"score_spread":0.2800806696043832,"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."}}