{"id":"W2075532062","doi":"10.1002/hec.1295","title":"Does age or life expectancy better predict health care expenditures?","year":2007,"lang":"en","type":"article","venue":"Health Economics","topic":"Global Health Care Issues","field":"Health Professions","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics","funders":"National Institute on Aging","keywords":"Life expectancy; Predictive power; Health care; Censoring (clinical trials); Expectancy theory; Longevity; Gerontology; Demography; Actuarial science; Psychology; Medicine; Econometrics; Economics; Environmental health; Sociology; Population; Social psychology; Economic growth","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002292645,0.0005400237,0.001291482,0.0003130766,0.002044453,0.00003159559,0.0005491728,0.0005421758,0.00116826],"category_scores_gemma":[0.0002311027,0.0003839421,0.000162553,0.0001767938,0.0001139058,0.0002844576,0.0002431274,0.001380768,0.001317475],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004234815,"about_ca_system_score_gemma":0.005962971,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003605312,"about_ca_topic_score_gemma":0.05147091,"domain_scores_codex":[0.9920025,0.0006775226,0.002895959,0.001021103,0.0002849925,0.003117908],"domain_scores_gemma":[0.9939252,0.000920667,0.001439597,0.001147235,0.0001419761,0.002425299],"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":[0.00220237,0.0002582963,0.09298483,0.008214091,0.000100913,0.000128111,0.08680631,0.00001539053,0.000001215938,0.005070766,0.7764257,0.02779198],"study_design_scores_gemma":[0.002052448,0.000757253,0.04907629,0.0004933757,0.000007283707,0.0000060759,0.0414598,0.00001597061,0.000005665107,0.0002390315,0.9054245,0.0004623285],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8307403,0.0185398,0.0005879353,0.08995317,0.02876757,0.01020038,0.001295116,0.00162639,0.01828932],"genre_scores_gemma":[0.6796266,0.005701132,0.007109375,0.2951579,0.008549713,0.000483488,0.0006046507,0.0002936909,0.002473455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2052047,"threshold_uncertainty_score":0.9998612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05510528327751662,"score_gpt":0.4373206027320131,"score_spread":0.3822153194544964,"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."}}