{"id":"W4399481771","doi":"10.1136/annrheumdis-2024-eular.1667","title":"OP0015 THE ROLE OF MULTI-CRITERIA DECISION ANALYSIS IN THE DEVELOPMENT OF CANDIDATE CLASSIFICATION CRITERIA FOR ANTISYNTHETASE SYNDROME: ANALYSIS FROM THE CLASS PROJECT","year":2024,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Jewish General Hospital","funders":"","keywords":"Antisynthetase syndrome; Computer science; Class (philosophy); Artificial intelligence; Linguistics; Philosophy","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":[0.001630251,0.0001757762,0.0005965239,0.0002239742,0.0001569085,0.00004602917,0.000874844,0.00009894728,0.0001648803],"category_scores_gemma":[0.0007549258,0.00006890946,0.0006697464,0.001603746,0.0003808422,0.00005902969,0.0001330357,0.0001114535,0.000002223951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001121602,"about_ca_system_score_gemma":0.0001270086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003594501,"about_ca_topic_score_gemma":0.0004066738,"domain_scores_codex":[0.9975325,0.0009833038,0.0008691319,0.0002685063,0.0001379264,0.0002085775],"domain_scores_gemma":[0.9959096,0.002840102,0.0003366573,0.0007670564,0.0001312082,0.00001540221],"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":[0.002950016,0.005629924,0.08189236,0.001491619,0.100286,0.000006338074,0.02862116,0.0003201678,0.07699597,0.0047597,0.0192966,0.6777502],"study_design_scores_gemma":[0.000712706,0.0001305161,0.8949063,0.0009800316,0.01509638,0.000003948796,0.006791834,0.03567389,0.03556582,0.005390601,0.004423252,0.0003247392],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797066,0.01136632,0.003335383,0.00238703,0.000170724,0.0007934001,0.002201153,0.0000114864,0.00002792626],"genre_scores_gemma":[0.9980811,0.0002621808,0.001280479,0.000112553,0.000004608769,0.00007497978,0.0001429987,0.000008245778,0.00003285936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8130139,"threshold_uncertainty_score":0.2810046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08484319331023812,"score_gpt":0.3968394934648648,"score_spread":0.3119963001546266,"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."}}