{"id":"W2090238015","doi":"10.1016/j.healthpol.2004.05.007","title":"The macro determinants of health expenditure in the United States and Canada: assessing the impact of income, age distribution and time","year":2004,"lang":"en","type":"article","venue":"Health Policy","topic":"Global Health Care Issues","field":"Health Professions","cited_by":219,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"Health Canada","keywords":"Proxy (statistics); Demographic economics; Economics; Per capita; Baby boom; Distribution (mathematics); Population; Cohort effect; Population ageing; Demography; Statistics; Sociology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003357145,0.0001887987,0.0004922323,0.00009929626,0.001581753,0.00001827418,0.0002571539,0.0001068983,0.000003672782],"category_scores_gemma":[0.0004852936,0.00009253803,0.00003163734,0.0005645781,0.0002522531,0.00007816663,0.0001148397,0.0005916613,0.000001530922],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735061,"about_ca_system_score_gemma":0.00875733,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9880857,"about_ca_topic_score_gemma":0.8117678,"domain_scores_codex":[0.9950888,0.002311136,0.001122466,0.0001877988,0.0003399088,0.0009498912],"domain_scores_gemma":[0.9966974,0.001612147,0.0009516812,0.0004011359,0.0001151167,0.000222579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002794245,0.0002714081,0.7719893,0.006317066,0.00006601431,0.00004156112,0.1209505,0.0003982158,0.0000179143,0.009990577,0.06984304,0.01983492],"study_design_scores_gemma":[0.0006987799,0.0002435041,0.9829859,0.0007995857,0.000003134416,0.00001352093,0.009081329,0.000130122,0.000002918214,0.001392195,0.004573487,0.00007555103],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559772,0.002145412,0.000007359753,0.03985803,0.00009043056,0.001313726,0.0005363768,0.00001283061,0.00005860775],"genre_scores_gemma":[0.9930481,0.002277995,0.00001989741,0.004376722,0.00009235093,0.00003577109,0.0001146079,0.00001518057,0.00001934527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2109965,"threshold_uncertainty_score":0.9997181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04091596578520171,"score_gpt":0.495927484684089,"score_spread":0.4550115188988873,"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."}}