{"id":"W2670513570","doi":"10.1016/j.socscimed.2017.06.024","title":"Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework","year":2017,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Technology Assessment international; European Commission","keywords":"Multiple-criteria decision analysis; Health technology; Decision analysis; Value (mathematics); Management science; Medicine; Operations research; Health care; Engineering; Mathematics; Statistics; Economics; Economic growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04803174,0.0001670109,0.001114276,0.0006975305,0.002264404,0.0001183251,0.0008639129,0.0001355208,0.00008588623],"category_scores_gemma":[0.03803451,0.0001429187,0.00006844385,0.0009679716,0.001298208,0.0004872493,0.0001307251,0.0002647034,0.000006738359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006695946,"about_ca_system_score_gemma":0.0006102783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00205971,"about_ca_topic_score_gemma":0.0008583782,"domain_scores_codex":[0.9958497,0.0001641244,0.002375551,0.0007227543,0.0003186055,0.0005692427],"domain_scores_gemma":[0.9940906,0.002373699,0.002521351,0.0006784035,0.0001277219,0.0002082356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003059597,0.00005337715,0.5040088,0.0001327611,0.00006827898,6.036857e-7,0.01951532,0.0001294067,0.00004178939,0.421434,0.004988253,0.04959678],"study_design_scores_gemma":[0.001793246,0.0003082811,0.4769484,0.0002550275,0.00003560695,0.000001490956,0.00913162,0.05719601,0.000001943564,0.4479779,0.00611333,0.000237154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2731568,0.003493789,0.140422,0.5792598,0.001373985,0.001364059,0.00003815616,0.0000348759,0.0008566037],"genre_scores_gemma":[0.9644462,0.0001913349,0.0212025,0.01324682,0.0007282827,0.000107804,0.000005941663,0.00001329395,0.00005784468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6912894,"threshold_uncertainty_score":0.9990345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3152810283105041,"score_gpt":0.5764326961852816,"score_spread":0.2611516678747774,"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."}}