{"id":"W2992272781","doi":"10.1377/hlthaff.2019.00915","title":"Healthy People 2020: Rural Areas Lag In Achieving Targets For Major Causes Of Death","year":2019,"lang":"en","type":"article","venue":"Health Affairs","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"National Institutes of Health","keywords":"Rural area; Medicine; Cause of death; Environmental health; Demography; Gerontology; Geography; Disease; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003524199,0.0005147819,0.000287503,0.0008950441,0.000751035,0.001570917,0.0007910595,0.001064631,0.01230981],"category_scores_gemma":[0.003548049,0.0002696735,0.0005436451,0.0008258416,0.0003200074,0.001997371,0.003759039,0.001486399,0.002257366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288248,"about_ca_system_score_gemma":0.005900333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03170628,"about_ca_topic_score_gemma":0.04361558,"domain_scores_codex":[0.9990917,0.0002867884,0.000032848,0.00006891815,0.000174006,0.0003456469],"domain_scores_gemma":[0.9989539,0.0001201543,0.0001497461,0.00003334125,0.0001991194,0.0005436871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001863566,0.0007194793,0.1662051,0.001431238,0.0002818176,0.0004270572,0.002222892,0.003371394,0.002690372,0.03427717,0.4568297,0.3296804],"study_design_scores_gemma":[0.001013404,0.001893124,0.4625079,0.0009983868,0.0002724697,0.0004098247,0.006077973,0.007885306,0.002382947,0.02465473,0.4917908,0.0001130472],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5293415,0.01100676,0.02137754,0.2024644,0.00650944,0.0008457819,0.06632867,0.003497185,0.1586288],"genre_scores_gemma":[0.9401485,0.002457836,0.009857473,0.01386702,0.0006111635,0.0006368855,0.01557278,0.0002063741,0.01664198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03170628,"threshold_uncertainty_score":0.06304342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02035998510983384,"score_gpt":0.3453821443633214,"score_spread":0.3250221592534875,"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."}}