{"id":"W4379802644","doi":"10.1136/annrheumdis-2023-eular.3915","title":"AB0823 A BAYESIAN APPROACH TO DETERMINE THE ROLE OF ORAL ANTICOAGULANTS FOR THE OCCURRENCE OF DIGITAL ULCERS IN SYSTEMIC SCLEROSIS – A EUSTAR OBSERVATIONAL STUDY","year":2023,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Systemic Sclerosis and Related Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Boehringer Ingelheim (Canada)","funders":"Universität Zürich; Leids Universitair Medisch Centrum; Universitätsspital Zürich; Università degli Studi di Firenze; Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi di Firenze; Fundació la Marató de TV3; Universiteit Leiden; University of Bern","keywords":"Medicine; Observational study; Multiple sclerosis; Bayesian probability; Intensive care medicine; Dermatology; Internal medicine; Immunology; Artificial intelligence; Computer science","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":[0.01731099,0.0009564584,0.001870888,0.001484351,0.0007429337,0.001518336,0.00137834,0.001889262,0.004639051],"category_scores_gemma":[0.02934357,0.0008179352,0.003976587,0.001477367,0.0004102878,0.0008639173,0.001191773,0.001873315,0.0004637245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004511122,"about_ca_system_score_gemma":0.001300234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01809037,"about_ca_topic_score_gemma":0.01022762,"domain_scores_codex":[0.9831384,0.01400755,0.0005994551,0.001130591,0.0006250012,0.0004989085],"domain_scores_gemma":[0.9816049,0.0130219,0.001728184,0.001653983,0.001159715,0.0008312514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02571174,0.0009557791,0.8941932,0.0004135519,0.02586149,0.000784991,0.0003929129,0.0125756,0.001675949,0.003562678,0.002851927,0.03102015],"study_design_scores_gemma":[0.007958902,0.006854106,0.6541837,0.0005371041,0.02230039,0.001483414,0.0009447063,0.2865261,0.00056989,0.01257215,0.005851796,0.0002176193],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640962,0.002359573,0.02751153,0.001071414,0.0001417081,0.0002167196,0.002891985,0.00006903172,0.001641896],"genre_scores_gemma":[0.9878138,0.0004298597,0.0087458,0.0002070839,0.0001036212,0.000204463,0.001627382,0.00002254955,0.0008454034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01809037,"threshold_uncertainty_score":0.09155035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2142891824325421,"score_gpt":0.3473047127067337,"score_spread":0.1330155302741917,"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."}}