{"id":"W4411397737","doi":"10.1016/j.ard.2025.06.223","title":"POS0867 MACHINE LEARNING MODEL OUTPERFORMS CONVENTIONAL LOGISTIC REGRESSION IN PREDICTING SPINAL RADIOGRAPHIC PROGRESSION OVER 2-YEAR INTERVALS IN AXIAL SPONDYLOARTHRITIS","year":2025,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Medicine; Axial spondyloarthritis; Logistic regression; Radiography; Machine learning; Radiology; Internal medicine; Magnetic resonance imaging; Sacroiliitis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004156005,0.0001583983,0.0003448391,0.0003676288,0.00006175454,0.00002923182,0.0002375293,0.0000539693,0.00005902422],"category_scores_gemma":[0.000728808,0.0001083251,0.0002211565,0.0005299032,0.0001197901,0.0001298631,0.00009257018,0.0002805405,0.000001638057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002013202,"about_ca_system_score_gemma":0.00003736492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006934539,"about_ca_topic_score_gemma":0.00001451533,"domain_scores_codex":[0.9986091,0.0001108883,0.0005370174,0.0001691177,0.0003281333,0.0002456949],"domain_scores_gemma":[0.999497,0.00009770406,0.0001060706,0.0001925468,0.00003482975,0.00007187846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001162886,0.000277036,0.7940886,0.001381105,0.0001386555,0.00001084893,0.0001819793,0.127647,0.00008822349,0.0003843764,0.0006967028,0.07498922],"study_design_scores_gemma":[0.0006897933,0.0000257011,0.1281376,0.01051796,0.00003986327,0.000001083952,0.00006512739,0.8573264,0.00009379885,0.00298398,0.00001233991,0.0001063309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928046,0.005084914,0.001168789,0.0003651055,0.0001490822,0.000154703,0.00002733436,0.00006695577,0.0001784966],"genre_scores_gemma":[0.9988892,0.0007796077,0.0001295813,0.00004209451,0.00001306761,0.00002358467,0.00002522297,0.00001316944,0.00008447566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7296795,"threshold_uncertainty_score":0.4417368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447656724590365,"score_gpt":0.3146801968492783,"score_spread":0.2902036296033747,"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."}}