{"id":"W3197379691","doi":"10.1016/j.semarthrit.2021.08.011","title":"The evolution of instrument selection for inclusion in core outcome sets at OMERACT: Filter 2.2","year":2021,"lang":"en","type":"article","venue":"Seminars in Arthritis and Rheumatism","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; Ottawa Hospital; Canada Research Chairs; Institute for Work & Health; Ottawa Public Health; SickKids Foundation; University of Toronto; Wilfrid Laurier University; University of Ottawa","funders":"National Institute for Health and Care Research; Parker Institute for Cancer Immunotherapy; Leeds Biomedical Research Centre; Agence Nationale de la Recherche; Oak Foundation","keywords":"Medicine; Core (optical fiber); Selection (genetic algorithm); Outcome (game theory); Inclusion (mineral); Filter (signal processing); Medical physics; Artificial intelligence; Mathematical economics; Optics","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":[0.0019081,0.00006707829,0.0001509422,0.0001002753,0.0006723939,0.0000332514,0.0001221953,0.0001073532,0.00004314385],"category_scores_gemma":[0.0006227996,0.00005948168,0.00003306614,0.0003394422,0.0001927605,0.0001219074,0.0004487651,0.0001376547,0.00000124824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004630407,"about_ca_system_score_gemma":0.0001177823,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003363171,"about_ca_topic_score_gemma":0.052519,"domain_scores_codex":[0.998702,0.0001575231,0.0003507843,0.0001729828,0.0003613432,0.000255346],"domain_scores_gemma":[0.9992142,0.0004650829,0.00008907619,0.0001025355,0.00008904117,0.00004000583],"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":[0.00002881385,0.00004823396,0.1347408,0.00002709279,0.000002844491,0.00000474576,0.006449359,0.000005100583,0.00128003,0.008734603,0.001275158,0.8474032],"study_design_scores_gemma":[0.005714504,0.0007611068,0.5293657,0.02673111,0.00001144569,0.0001374945,0.03667726,0.01036464,0.01203337,0.3341751,0.04299683,0.001031538],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815382,0.01413521,0.00009667457,0.002231513,0.0001855502,0.0004743135,0.00001389013,0.00001591121,0.001308673],"genre_scores_gemma":[0.9408437,0.05714243,0.001379298,0.00002003402,0.00000966131,0.00007409475,0.000006260011,0.000006543152,0.0005179843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8463717,"threshold_uncertainty_score":0.9647701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06816426863444106,"score_gpt":0.4001558913553581,"score_spread":0.3319916227209171,"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."}}