{"id":"W2978965473","doi":"10.1016/j.jval.2019.08.008","title":"Quebec Health-Related Quality-of-Life Population Norms Using the EQ-5D-5L: Decomposition by Sociodemographic Data and Health Problems","year":2019,"lang":"en","type":"article","venue":"Value in Health","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Sherbrooke; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre Hospitalier Universitaire de Sherbrooke","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Medicine; Demography; Confidence interval; Quality of life (healthcare); Disadvantaged; Population; Interquartile range; Gerontology; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.06248575,0.0002788525,0.001854215,0.0004902806,0.0004718195,0.00007955605,0.0005036819,0.0002037762,0.00005323494],"category_scores_gemma":[0.0006876325,0.0002977088,0.00009280531,0.0005971573,0.0001020806,0.0007436525,0.0001486571,0.0004623045,0.00006744083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001899604,"about_ca_system_score_gemma":0.001378773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6570507,"about_ca_topic_score_gemma":0.03226337,"domain_scores_codex":[0.9859977,0.002808301,0.009184225,0.0009781605,0.00023571,0.000795866],"domain_scores_gemma":[0.9897705,0.001110604,0.0075101,0.001196334,0.00004719043,0.0003652944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000309746,0.0002566252,0.8925234,0.006340598,0.00008393854,8.234112e-8,0.01017956,0.01330096,0.00000391738,0.06977087,0.006621256,0.0008878223],"study_design_scores_gemma":[0.003040646,0.0004081877,0.5906968,0.001520136,0.000004922792,0.000009303543,0.003400652,0.3740904,3.883783e-7,0.02232285,0.003809135,0.0006965774],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8434023,0.02533881,0.002239209,0.1247603,0.0006697811,0.002718953,0.0007844842,0.00005371351,0.00003241755],"genre_scores_gemma":[0.9805632,0.001751216,0.002119483,0.01440384,0.00009423224,0.00003338009,0.0009193837,0.00005584598,0.00005943624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6247874,"threshold_uncertainty_score":0.9999475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5531132612288412,"score_gpt":0.457097136029606,"score_spread":0.09601612519923519,"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."}}