{"id":"W2405193268","doi":"10.1097/mlr.0000000000000447","title":"A Time Trade-off-derived Value Set of the EQ-5D-5L for Canada","year":2015,"lang":"en","type":"article","venue":"Medical Care","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":485,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Alberta; St. Joseph’s Healthcare Hamilton; University of British Columbia; Hospital for Sick Children; Institute for Clinical Evaluative Sciences; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"EQ-5D; Censoring (clinical trials); Socioeconomic status; Population; Statistics; Time-trade-off; Dimension (graph theory); Demographics; Demography; Mathematics; Econometrics; Medicine; Quality of life (healthcare); 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"],"consensus_categories":[],"category_scores_codex":[0.005672952,0.0001289238,0.0006786851,0.00005247031,0.00009959795,0.0000134129,0.0004679251,0.0001604516,0.0003462254],"category_scores_gemma":[0.008812346,0.0001214613,0.0001233942,0.0001157636,0.0001114833,0.00007331317,0.00006318121,0.0001573897,0.0001127706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007462071,"about_ca_system_score_gemma":0.002362626,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07948496,"about_ca_topic_score_gemma":0.0962715,"domain_scores_codex":[0.9970281,0.0002170287,0.001869795,0.0003040314,0.0002688914,0.0003122124],"domain_scores_gemma":[0.9976035,0.0007264016,0.0007661127,0.0004324211,0.00007992404,0.0003916109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003664803,0.00005089557,0.01756839,0.0009918266,0.0001293367,0.000003243057,0.01712734,0.0003009167,0.000003065773,0.02161418,0.9403481,0.001826068],"study_design_scores_gemma":[0.002292959,0.0001342318,0.01090204,0.000154134,0.00001824639,0.000008208957,0.008820419,0.01522599,0.00004345275,0.003228613,0.958805,0.0003666612],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7085555,0.007501199,0.0007187567,0.2680742,0.00307861,0.00189155,0.003305165,0.00005216879,0.006822866],"genre_scores_gemma":[0.9779029,0.00001516484,0.0003630932,0.02027542,0.0006595476,0.0001111955,0.000103407,0.00003208574,0.0005372006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2693474,"threshold_uncertainty_score":0.9995369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2760068157768919,"score_gpt":0.3783320079101588,"score_spread":0.1023251921332669,"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."}}