{"id":"W2911540048","doi":"10.3899/jrheum.181218","title":"Instrument Selection Using the OMERACT Filter 2.1: The OMERACT Methodology","year":2019,"lang":"en","type":"article","venue":"The Journal of Rheumatology","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health; University of Ottawa","funders":"Leeds Biomedical Research Centre; School of Medicine, University of Alabama at Birmingham; Eli Lilly Australia; Vrije Universiteit Amsterdam; Sorbonne Université; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; Pfizer Australia; 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":"Construct validity; Filter (signal processing); Set (abstract data type); Population; Computer science; Selection (genetic algorithm); Medical physics; Psychology; Artificial intelligence; Medicine; Psychometrics; Clinical psychology","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":[0.3712288,0.002322189,0.003831455,0.00853398,0.003039054,0.006160844,0.002720494,0.003610166,0.02910002],"category_scores_gemma":[0.5608722,0.002122347,0.009104598,0.008651058,0.002267579,0.00399386,0.007142958,0.005096021,0.008329864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004558766,"about_ca_system_score_gemma":0.02352463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002643151,"about_ca_topic_score_gemma":0.002724817,"domain_scores_codex":[0.6326439,0.2805625,0.04609766,0.009355395,0.02778243,0.00355805],"domain_scores_gemma":[0.4803361,0.3361643,0.03072901,0.05896864,0.09143391,0.002368013],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004751425,0.0007316303,0.02223615,0.01385675,0.001329466,0.0003279083,0.01061257,0.003354821,0.003455316,0.05158556,0.1809479,0.7068105],"study_design_scores_gemma":[0.00687128,0.004031149,0.0928045,0.02706545,0.002262087,0.0007782517,0.003551949,0.02252338,0.01655756,0.1014183,0.7213114,0.0008246583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01288695,0.001570426,0.7939222,0.004727272,0.001678327,0.1560289,0.01360424,0.002930567,0.01265103],"genre_scores_gemma":[0.01824377,0.0005013947,0.6297737,0.002133864,0.0004088258,0.339342,0.00574479,0.001195433,0.002656223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6287712,"threshold_uncertainty_score":0.7753869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2319471776990591,"score_gpt":0.4701745187867887,"score_spread":0.2382273410877296,"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."}}