{"id":"W3036629528","doi":"10.1016/j.clnesp.2020.05.008","title":"What do screening tools measure? Lessons learned from SCREEN II and SNAQ65+","year":2020,"lang":"en","type":"article","venue":"Clinical Nutrition ESPEN","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"FrieslandCampina","keywords":"Medicine; Measure (data warehouse); Medical physics; Data mining; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.1451671,0.002047453,0.008397081,0.004011682,0.002045586,0.01025475,0.005450233,0.00592165,0.003736338],"category_scores_gemma":[0.2627228,0.001467096,0.004832113,0.003457824,0.005580785,0.01534049,0.005063101,0.01349373,0.001028315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005608281,"about_ca_system_score_gemma":0.01302539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02930904,"about_ca_topic_score_gemma":0.04493254,"domain_scores_codex":[0.9366972,0.03810341,0.008716061,0.00568564,0.009097782,0.001699904],"domain_scores_gemma":[0.622234,0.3109629,0.009856331,0.009108916,0.04272177,0.005116065],"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":[0.001532335,0.0006007087,0.1779675,0.006678932,0.00442373,0.0003519217,0.005434508,0.001961477,0.000258126,0.02184068,0.07457303,0.7043771],"study_design_scores_gemma":[0.002440847,0.004190929,0.2792885,0.08648646,0.0122561,0.002221558,0.02542979,0.02217215,0.002060472,0.3205155,0.2412839,0.001653965],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07440852,0.4206287,0.03890443,0.4291101,0.01556594,0.0006969325,0.001857998,0.0004531192,0.0183743],"genre_scores_gemma":[0.5399363,0.1649777,0.105531,0.1558543,0.02520905,0.002243901,0.001691236,0.0007363647,0.003820084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1451671,"threshold_uncertainty_score":0.7677262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4751582164721248,"score_gpt":0.4785472104282438,"score_spread":0.003388993956119024,"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."}}