{"id":"W2982578867","doi":"10.1287/msom.2019.0807","title":"An Achievable-Region-Based Approach for Kidney Allocation Policy Design with Endogenous Patient Choice","year":2020,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ranking (information retrieval); Equity (law); Computer science; Class (philosophy); Kidney transplant; Quality (philosophy); Relevance (law); Affine transformation; Microeconomics; Operations research; Mathematical optimization; Actuarial science; Kidney; Economics; Medicine; Kidney transplantation; Machine learning; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005549077,0.0002591193,0.0002233948,0.0001471651,0.0003231189,0.0001338248,0.0001456387,0.00004434731,0.00001982495],"category_scores_gemma":[0.000006378564,0.0002024498,0.00006118226,0.0002366699,0.00001353821,0.0002090163,0.00001571917,0.00008619409,0.00001709503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279768,"about_ca_system_score_gemma":0.0001286866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003997191,"about_ca_topic_score_gemma":0.00002463754,"domain_scores_codex":[0.9986584,0.00005348431,0.0002683252,0.0004646504,0.0002807541,0.0002744158],"domain_scores_gemma":[0.9990993,0.0000314231,0.00005911451,0.0003705469,0.0001042269,0.0003353625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008616211,0.001008629,0.0007103565,0.001994438,0.0005580773,0.00005051335,0.001417121,0.9868292,0.000666443,0.0006462515,0.00007314624,0.005184218],"study_design_scores_gemma":[0.03054405,0.007915663,0.0178777,0.0007738902,0.003289355,0.0001190113,0.002288311,0.7700458,0.1529814,0.00004957758,0.01243045,0.001684828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03114517,0.00001685822,0.9454583,0.01611671,0.00003614032,0.00493309,0.00004594871,0.0002351371,0.002012674],"genre_scores_gemma":[0.8591794,0.00001333285,0.119822,0.01857238,0.00008062855,0.0007657465,0.001401675,0.00004667892,0.0001182314],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8280342,"threshold_uncertainty_score":0.8255662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05601405125973238,"score_gpt":0.2723650891827546,"score_spread":0.2163510379230222,"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."}}