{"id":"W3035148408","doi":"10.1111/all.14444","title":"SF‐6Dv2 preference value set for health utility in food allergy","year":2020,"lang":"en","type":"article","venue":"Allergy","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Institut Universitaire en Santé Mentale de Québec; Université de Montréal","funders":"Canadian Allergy, Asthma and Immunology Foundation","keywords":"Preference; EQ-5D; Medicine; Quality of life (healthcare); Population; Set (abstract data type); Time-trade-off; Cohort; Food allergy; Dimension (graph theory); Quality-adjusted life year; Demography; Allergy; Statistics; Environmental health; Health related quality of life; Mathematics; Computer science; Cost effectiveness; Internal medicine; Immunology; Risk analysis (engineering)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0125128,0.0005896169,0.0006961189,0.001280662,0.0002773725,0.0009579472,0.0007035607,0.0004376793,0.003544037],"category_scores_gemma":[0.04706601,0.0001852673,0.00192311,0.001190708,0.000462501,0.0005635348,0.0008388462,0.001046918,0.0003153127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001672679,"about_ca_system_score_gemma":0.0008913297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005509661,"about_ca_topic_score_gemma":0.005350593,"domain_scores_codex":[0.9911047,0.006229652,0.0004875443,0.0002908149,0.001736575,0.0001506583],"domain_scores_gemma":[0.9781685,0.01711521,0.001162694,0.001360689,0.001961369,0.0002315825],"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":[0.007185603,0.00119755,0.3841843,0.001882679,0.00206829,0.0003079213,0.002012732,0.1375131,0.006973514,0.01575157,0.01015146,0.4307713],"study_design_scores_gemma":[0.00110862,0.005000347,0.4852645,0.0007626876,0.0004829825,0.0007246796,0.001380159,0.4556879,0.007167589,0.02792112,0.01409727,0.0004021719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.867661,0.0005666466,0.1169926,0.0003595497,0.00007999908,0.00142074,0.005486262,0.0001407794,0.007292504],"genre_scores_gemma":[0.9547688,0.00009440484,0.04128745,0.00006665308,0.00001154526,0.001110012,0.002238702,0.00001397985,0.000408566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0125128,"threshold_uncertainty_score":0.06617481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5724675847802668,"score_gpt":0.4312859822202083,"score_spread":0.1411816025600586,"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."}}