{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006861662,0.00115125,0.001405083,0.001415061,0.0003474937,0.001013183,0.0008828355,0.0009922751,0.002629895],"category_scores_gemma":[0.007026259,0.0002773551,0.00144632,0.0007478473,0.0002296267,0.0007844211,0.0006096872,0.0011266,0.00139696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000427488,"about_ca_system_score_gemma":0.001146068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004626741,"about_ca_topic_score_gemma":0.003198359,"domain_scores_codex":[0.9985165,0.0007324971,0.0001315977,0.0002906193,0.0002148612,0.0001138789],"domain_scores_gemma":[0.9965821,0.002280604,0.0002475916,0.0001836203,0.0005460056,0.0001601293],"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":[0.004618884,0.00164895,0.2880187,0.00060029,0.001184976,0.0004775279,0.0001397037,0.3656137,0.00253567,0.0005781291,0.01409475,0.3204887],"study_design_scores_gemma":[0.00006951521,0.001414277,0.02161531,0.0001047545,0.0001907512,0.0001723632,0.00006176298,0.9737334,0.0008086502,0.0005964168,0.001205393,0.00002735921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9434428,0.006540928,0.03877743,0.001417845,0.000597135,0.0001357996,0.002260193,0.001971243,0.004856832],"genre_scores_gemma":[0.9826024,0.0005136079,0.01177014,0.0001860115,0.0001067056,0.00007328268,0.002883018,0.00006620526,0.001798564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006861662,"threshold_uncertainty_score":0.03628832,"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."}}