{"id":"W2254177296","doi":"","title":"Predicting Expenditures for Persons With Chronic Conditions","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Global Health Care Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Distribution (mathematics); Health care; Medical care; Demographic economics; Population; Actuarial science; Chronic disease; Work (physics); Public economics; Medicine; Business; Gerontology; Economics; Environmental health; Family medicine; Economic growth; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006260498,0.0004446147,0.000306966,0.002357888,0.0003835937,0.0007663384,0.0004205038,0.0007060344,0.006434285],"category_scores_gemma":[0.004170516,0.0002303758,0.0009690062,0.001543032,0.0001527143,0.0005839721,0.0005679844,0.001115619,0.0009003098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007138287,"about_ca_system_score_gemma":0.0006048909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03425063,"about_ca_topic_score_gemma":0.02814949,"domain_scores_codex":[0.9996394,0.00008143969,0.00004922839,0.000076642,0.00006347177,0.00008979494],"domain_scores_gemma":[0.9983397,0.000463991,0.0005123454,0.00007572069,0.0002526607,0.0003555758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004510103,0.0000678611,0.9961163,0.000007706051,0.00003234325,0.00002745248,0.00001505296,0.000619108,0.00002166767,0.00007800938,0.001025213,0.001944044],"study_design_scores_gemma":[0.00001098477,0.00006338803,0.9890507,0.00001908199,0.00003684185,0.0001122713,0.0001610319,0.009680308,0.00005423577,0.000176839,0.0006282197,0.000006024726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877928,0.0003313533,0.0004032237,0.0005966217,0.00002771513,0.0000448423,0.00814767,0.00003608565,0.002619712],"genre_scores_gemma":[0.989495,0.0002865361,0.0006498127,0.0000575144,0.00003599022,0.00003079966,0.008825446,0.000005263279,0.0006137866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03425063,"threshold_uncertainty_score":0.06810254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364191383128637,"score_gpt":0.4142190371831719,"score_spread":0.3905771233518855,"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."}}