{"id":"W2100021105","doi":"10.1002/hec.3178","title":"Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution","year":2015,"lang":"en","type":"article","venue":"Health Economics","topic":"Global Health Care Issues","field":"Health Professions","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; University of Toronto","keywords":"Counterfactual thinking; Econometrics; Parametric statistics; Statistics; Health care; Distribution (mathematics); Monte Carlo method; Probability distribution; Economics; Computer science; Actuarial science; Mathematics; Psychology","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005378345,0.000325488,0.0005622326,0.00006358657,0.003386552,0.0000496359,0.0006969682,0.0002539467,0.00004497631],"category_scores_gemma":[0.000830099,0.0002076182,0.0000843159,0.0003496549,0.0001212339,0.0001815566,0.0004212622,0.001202694,0.003204706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003602277,"about_ca_system_score_gemma":0.003820062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004070213,"about_ca_topic_score_gemma":0.03697337,"domain_scores_codex":[0.9945257,0.001512043,0.001464485,0.0005689846,0.0002617649,0.001666985],"domain_scores_gemma":[0.9949569,0.001113281,0.0008093573,0.001233912,0.0004305228,0.0014561],"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":[0.0003469613,0.00005413223,0.009580824,0.0005679725,0.00002077342,0.000003631758,0.02394741,0.0007171047,6.212409e-7,0.06149844,0.8844822,0.01877991],"study_design_scores_gemma":[0.0007698837,0.000328056,0.008611518,0.0004711963,0.00001108908,0.00001707212,0.01326974,0.002199218,0.000002744228,0.004518516,0.9695456,0.0002553826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2803533,0.005551489,0.0007009761,0.6910789,0.008207992,0.008658441,0.001153934,0.0004083489,0.003886617],"genre_scores_gemma":[0.8370704,0.001778631,0.001910405,0.1525847,0.002457795,0.001813692,0.0007889929,0.0001565513,0.001438875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5567171,"threshold_uncertainty_score":0.9979109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1288208916957647,"score_gpt":0.4965153287850331,"score_spread":0.3676944370892684,"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."}}