{"id":"W3165603187","doi":"10.1136/annrheumdis-2021-eular.3343","title":"POS0036 AN ARTIFICIAL INTELLIGENCE MODEL IN RHEUMATOLOGY: INTERPRETATION OF THE SACROILIAC JOINT GRAPHY IN ANKYLOSING SPONDYLITIS","year":2021,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Alberta; Université de Sherbrooke; University of Calgary","keywords":"Ankylosing spondylitis; Medicine; Radiography; Sacroiliac joint; Radiology; Artificial intelligence; Rheumatology; Physical therapy; Medical physics; Internal medicine; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0004482385,0.0003555867,0.0002912545,0.0007023109,0.000288944,0.001197258,0.0005379558,0.0007359224,0.004970403],"category_scores_gemma":[0.002314672,0.0001604447,0.0006563165,0.0004784678,0.0003823416,0.0007441176,0.0004857872,0.0005823378,0.0005855363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000648061,"about_ca_system_score_gemma":0.0009614679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0106817,"about_ca_topic_score_gemma":0.01188885,"domain_scores_codex":[0.9997899,0.00008442712,0.00001401422,0.00004778661,0.00004876528,0.00001506603],"domain_scores_gemma":[0.9995471,0.0002679481,0.00002874514,0.00003397655,0.00009428134,0.00002804768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003840206,0.0001851059,0.01121728,0.0002144488,0.0001515868,0.0008569621,0.000270289,0.7934825,0.005319358,0.0537244,0.008378686,0.1258152],"study_design_scores_gemma":[0.00001410174,0.00002960574,0.00106982,0.00001296947,0.00001881584,0.0000591356,0.00002654492,0.9740651,0.0005318612,0.02223485,0.001930784,0.000006278162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2159996,0.0006849484,0.7460364,0.004666604,0.0003019851,0.0001497989,0.003645268,0.002220565,0.02629487],"genre_scores_gemma":[0.8692668,0.0002992259,0.1209298,0.000285917,0.00007537022,0.00009249766,0.001440599,0.00009259262,0.007517214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0106817,"threshold_uncertainty_score":0.02123904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04183304547471996,"score_gpt":0.2886013446776275,"score_spread":0.2467682992029076,"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."}}