{"id":"W6964170091","doi":"10.25384/sage.22214060","title":"sj-png-4-mdm-10.1177_0272989X231159381 – Supplemental material for Efficient Designs for Valuation Studies That Use Time Tradeoff (TTO) Tasks to Map Latent Utilities from Discrete Choice Experiments to the Interval Scale: Selection of Health States for TTO Tasks","year":2023,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Valuation (finance); Selection (genetic algorithm); Interval data; Interval (graph theory); Discrete choice; Medical decision making","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0112066,0.001178342,0.001262729,0.001912145,0.001039323,0.003135529,0.002444203,0.002869174,0.8421113],"category_scores_gemma":[0.06561933,0.001844172,0.0009605185,0.003300439,0.001119722,0.002602168,0.00285513,0.002997043,0.6962715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001675029,"about_ca_system_score_gemma":0.003448963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003356294,"about_ca_topic_score_gemma":0.007793361,"domain_scores_codex":[0.9937861,0.00224188,0.0006598922,0.0007796764,0.002157835,0.0003747295],"domain_scores_gemma":[0.9355438,0.04269914,0.002343644,0.01054912,0.006671672,0.002192665],"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":[0.0003901394,0.0002059132,0.0006955962,0.0003861884,0.00003697948,0.00002072601,0.00007297162,0.0003689474,0.0005917815,0.004027455,0.9505332,0.0426701],"study_design_scores_gemma":[0.001714363,0.000234494,0.005008691,0.0004867242,0.00004256601,0.000076356,0.0001528059,0.00219011,0.002591891,0.02158773,0.9658253,0.0000889462],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.001492011,0.0001580517,0.04875099,0.002480049,0.0008254662,0.001615433,0.7547159,0.03105586,0.1589062],"genre_scores_gemma":[0.02254998,0.0005122141,0.1095968,0.003564889,0.0006426593,0.01429637,0.5468372,0.03797217,0.2640279],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8421113,"threshold_uncertainty_score":0.2252089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2747712861074186,"score_gpt":0.4341064754180095,"score_spread":0.1593351893105908,"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."}}