{"id":"W3196236833","doi":"10.1016/j.semarthrit.2021.08.004","title":"Improving domain definition and outcome instrument selection: Lessons learned for OMERACT from imaging","year":2021,"lang":"en","type":"article","venue":"Seminars in Arthritis and Rheumatism","topic":"Rheumatoid Arthritis Research and Therapies","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Hospital for Sick Children; University of British Columbia; Institute for Work & Health; Research Canada; University of Ottawa; SickKids Foundation; University of Toronto","funders":"Leeds Biomedical Research Centre; National Institute for Health and Care Research; Agence Nationale de la Recherche; UCB; Gilead Sciences; Roche; Merck; Pfizer","keywords":"Medicine; Outcome (game theory); Medical physics; Selection (genetic algorithm); Artificial intelligence; Mathematical economics","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":[0.1459168,0.002288032,0.004647077,0.004494804,0.0007828537,0.008607239,0.003191125,0.002190314,0.005339276],"category_scores_gemma":[0.2834341,0.001112685,0.00475462,0.004522216,0.001758028,0.007709223,0.006148736,0.009394289,0.001987889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233114,"about_ca_system_score_gemma":0.004956859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004026882,"about_ca_topic_score_gemma":0.006487726,"domain_scores_codex":[0.9242051,0.05852869,0.006246334,0.003495982,0.006430818,0.001093101],"domain_scores_gemma":[0.7254879,0.2152077,0.006814742,0.02486497,0.02526493,0.002359824],"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":[0.001878416,0.000686447,0.05715151,0.003265346,0.002768841,0.0002015184,0.001698537,0.007653893,0.002594047,0.01132521,0.03728195,0.8734943],"study_design_scores_gemma":[0.003000609,0.002702828,0.1807211,0.01146468,0.004315362,0.003180646,0.003437524,0.2136535,0.01658377,0.3740738,0.1857025,0.001163608],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03584759,0.01670955,0.9115367,0.0216697,0.0009984206,0.001226373,0.003177637,0.003083797,0.005750245],"genre_scores_gemma":[0.1242996,0.0050789,0.8554228,0.005161016,0.00136084,0.001317587,0.004537898,0.001786478,0.001034888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8540832,"threshold_uncertainty_score":0.7716913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03204645105933977,"score_gpt":0.3053155357547223,"score_spread":0.2732690846953825,"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."}}