{"id":"W3083753684","doi":"10.1136/rmdopen-2020-001357","title":"Recognising the spectrum of scleromyositis: HEp-2 ANA patterns allow identification of a novel clinical subset with anti-SMN autoantibodies","year":2020,"lang":"en","type":"article","venue":"RMD Open","topic":"Inflammatory Myopathies and Dermatomyositis","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; Cégep de l'Abitibi Témiscamingue; Jewish General Hospital; Hôpital du Sacré-Cœur de Montréal; Université de Montréal; University of Calgary; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Institutes of Health Research; Scleroderma Association of British Columbia","keywords":"Medicine; IIf; Autoantibody; Interstitial lung disease; Anti-nuclear antibody; Myositis; Rheumatology; Scleroderma (fungus); Pathology; Myopathy; Overlap syndrome; Internal medicine; Disease; Lung; Immunology; Antibody","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006226014,0.0001480837,0.0005749174,0.00004111838,0.00009661901,0.00007260874,0.0003703372,0.00007663753,0.00008494978],"category_scores_gemma":[0.000106549,0.000100708,0.0001183821,0.0002085311,0.0001743172,0.0002131267,0.0001925441,0.0002069671,0.00001573553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001404322,"about_ca_system_score_gemma":0.0001314262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000297002,"about_ca_topic_score_gemma":0.00007839176,"domain_scores_codex":[0.9982733,0.00009079373,0.0008333282,0.0003007416,0.0003059666,0.0001959457],"domain_scores_gemma":[0.998727,0.0001023511,0.0004865056,0.0004363867,0.0001456834,0.0001020856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00105338,0.0004075305,0.9628819,0.0009303055,0.0004934982,0.0001485188,0.003184715,0.00003574522,0.02345146,0.0005745104,0.002213018,0.004625379],"study_design_scores_gemma":[0.002830798,0.0004531032,0.9693092,0.0007350809,0.000223591,0.0001059181,0.0009606418,0.001857956,0.02244465,0.00002262167,0.0008901317,0.0001662765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892257,0.0001095926,0.002001109,0.00689694,0.0001098945,0.0008108211,0.0001154989,0.0000294673,0.000700973],"genre_scores_gemma":[0.9975696,0.0001615553,0.0009616078,0.001030393,0.0001400241,0.00001021694,0.00005479246,0.00002658806,0.00004519388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008343926,"threshold_uncertainty_score":0.4106753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07752857529124575,"score_gpt":0.339296525072569,"score_spread":0.2617679497813232,"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."}}