{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007356824,0.0001923399,0.0006787632,0.00005330661,0.0002912212,0.0002175662,0.0001359936,0.0002857656,0.0006701665],"category_scores_gemma":[0.00118074,0.0001903096,0.0002001554,0.0002278879,0.0001872288,0.0005184899,0.0001514863,0.0007955181,0.0001396726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002097184,"about_ca_system_score_gemma":0.00006480622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001756782,"about_ca_topic_score_gemma":0.000006879424,"domain_scores_codex":[0.9975469,0.0002349748,0.0008542095,0.0006562078,0.0003882056,0.0003195142],"domain_scores_gemma":[0.9977884,0.0006927642,0.0001673038,0.000292614,0.0001798581,0.0008790306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005864218,0.00199803,0.02182121,0.0006785329,0.0002735608,0.0002173597,0.0008984551,0.000001258881,0.004765111,0.002771645,0.08970086,0.8710098],"study_design_scores_gemma":[0.0285022,0.001849325,0.05246817,0.005568684,0.00032413,0.00003508446,0.002700395,0.001108647,0.001183213,0.008253905,0.8973926,0.0006136587],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2296051,0.008632511,0.002625506,0.7550091,0.0005929067,0.001506528,0.0002016571,0.0004009289,0.001425816],"genre_scores_gemma":[0.8946543,0.02940761,0.01877479,0.05236192,0.003658318,0.00006034468,0.0008055496,0.0000666647,0.0002104391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8703961,"threshold_uncertainty_score":0.7760602,"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."}}