{"id":"W4398228954","doi":"10.46497/archrheumatol.2024.10401","title":"Correlation between clinical disease activity and sacroiliac magnetic resonance imaging detection in axial spondyloarthropathy","year":2024,"lang":"en","type":"article","venue":"Archives of Rheumatology","topic":"Spondyloarthritis Studies and Treatments","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spondyloarthropathy; Magnetic resonance imaging; Axial spondyloarthritis; Medicine; Sacroiliac joint; Radiology; Correlation; Disease; Nuclear magnetic resonance; Pathology; Sacroiliitis; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008358048,0.0003289149,0.0002855424,0.000753746,0.0002772361,0.0005553476,0.000253035,0.0005627161,0.00236254],"category_scores_gemma":[0.003544608,0.0002286961,0.0002237342,0.0004882239,0.0002940045,0.0004059532,0.0003453291,0.0004163192,0.00037004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045418,"about_ca_system_score_gemma":0.0001612316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006296932,"about_ca_topic_score_gemma":0.0009340713,"domain_scores_codex":[0.9993086,0.0002149997,0.000119548,0.000127486,0.000140632,0.00008858745],"domain_scores_gemma":[0.9967674,0.0007853577,0.001394485,0.0001158659,0.0004544551,0.0004823585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000637035,0.00002155492,0.999109,0.000005994675,0.00001722599,0.00006027957,0.00001240219,0.0000100556,0.0002509415,0.000001630213,0.0000131751,0.000434062],"study_design_scores_gemma":[0.000006007632,0.0002002383,0.9983593,0.000003984469,0.00001504648,0.001168955,0.00005731792,0.00006738982,0.00005995466,0.000003512391,0.00005617703,0.000002009991],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992762,0.0003419602,0.00005467727,0.00001549893,0.000006041257,0.000005563408,0.00005712342,0.000001646112,0.0002412757],"genre_scores_gemma":[0.9996263,0.00007644922,0.00007129257,0.00001308377,0.00001610637,0.000004635215,0.0001247707,6.068493e-7,0.00006683044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00236254,"threshold_uncertainty_score":0.007903516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117060993990281,"score_gpt":0.291578927150459,"score_spread":0.2798728277514309,"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."}}