{"id":"W2912250133","doi":"10.3899/jrheum.181097","title":"Core Domain Set Selection According to OMERACT Filter 2.1: The OMERACT Methodology","year":2019,"lang":"en","type":"article","venue":"The Journal of Rheumatology","topic":"Rheumatoid Arthritis Research and Therapies","field":"Medicine","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Leeds Biomedical Research Centre; Eli Lilly Australia; Vrije Universiteit Amsterdam; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; Pfizer Australia; European League Against Rheumatism; Amsterdam University Medical Centers; National Institute for Health and Care Research; University of Leeds; Laboratoire d'Excellence Inflamex; Sydney Medical School; Ottawa Hospital Research Institute; Pfizer; Johns Hopkins University; Eli Lilly and Company; University of Ottawa; U.S. Department of Veterans Affairs","keywords":"Set (abstract data type); Core (optical fiber); Computer science; Domain (mathematical analysis); Voting; Process (computing); Selection (genetic algorithm); Artificial intelligence; Mathematics; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2051266,0.001459377,0.00220399,0.01277575,0.004174423,0.008171438,0.003537172,0.003472477,0.0229514],"category_scores_gemma":[0.2748238,0.001234857,0.005426447,0.007105045,0.002848716,0.005310517,0.009254065,0.003448161,0.005571296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008615491,"about_ca_system_score_gemma":0.04436712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003844811,"about_ca_topic_score_gemma":0.006089807,"domain_scores_codex":[0.8674687,0.08392713,0.01953835,0.007291169,0.01850784,0.003266773],"domain_scores_gemma":[0.7636864,0.1178392,0.01798197,0.02087993,0.07520299,0.004409476],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002026481,0.0005884488,0.01629906,0.02672522,0.0007097736,0.0005611664,0.03424579,0.003057298,0.007750151,0.1171803,0.1025807,0.6882756],"study_design_scores_gemma":[0.001399735,0.001246449,0.0316024,0.02738931,0.001101574,0.0008655383,0.01381423,0.008551246,0.01578039,0.1252319,0.772574,0.0004431565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02693508,0.003603704,0.7431141,0.01288813,0.002075183,0.1500255,0.01007075,0.00157264,0.04971487],"genre_scores_gemma":[0.03792555,0.0009280729,0.764654,0.002925347,0.0003830467,0.1788391,0.005386537,0.0005412627,0.008417035],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7948735,"threshold_uncertainty_score":0.9802205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08445836828710301,"score_gpt":0.3680521659449832,"score_spread":0.2835937976578802,"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."}}