{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1812912,0.001133607,0.001721335,0.003781403,0.001694255,0.005775037,0.002768524,0.002730959,0.005761874],"category_scores_gemma":[0.3601754,0.000737704,0.002397662,0.003763132,0.001031952,0.002974351,0.003857943,0.002031139,0.001210104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003152632,"about_ca_system_score_gemma":0.006163157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01605353,"about_ca_topic_score_gemma":0.01140787,"domain_scores_codex":[0.9096267,0.06001482,0.006572894,0.007411835,0.01231639,0.004057415],"domain_scores_gemma":[0.62882,0.3150512,0.008757735,0.01838328,0.02651227,0.002475508],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00707401,0.001136047,0.4471701,0.002220431,0.003229904,0.000294171,0.01515403,0.01843154,0.01143846,0.0146192,0.02303955,0.4561925],"study_design_scores_gemma":[0.001668354,0.004605102,0.6909071,0.00192291,0.002618495,0.0005823524,0.006540253,0.2114345,0.02279695,0.02251832,0.03384976,0.0005559824],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7553242,0.0006908577,0.2232926,0.002335913,0.0003433049,0.003667638,0.00407481,0.001667436,0.008603199],"genre_scores_gemma":[0.7808869,0.0001202654,0.207172,0.0006888597,0.00007354876,0.004215998,0.003735442,0.0006237875,0.002483141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8187088,"threshold_uncertainty_score":0.9587709,"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."}}