{"id":"W4387026835","doi":"10.1016/j.ijcard.2023.131359","title":"Forecasting the mortality burden of coronary heart disease and stroke in Germany: National trends and regional inequalities","year":2023,"lang":"en","type":"article","venue":"International Journal of Cardiology","topic":"Health and Medical Studies","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie; Ministero dell’Istruzione, dell’Università e della Ricerca; University of Auckland; Narodowe Centrum Badań i Rozwoju; Norges Forskningsråd; Réseau de cancérologie Rossy; Health Research Board; Bundesministerium für Bildung und Forschung; Institut National de la Recherche Agronomique; ZonMw","keywords":"Medicine; Stroke (engine); Demography; Mortality rate; Population; Population ageing; Cohort; Environmental health; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00184928,0.00006317747,0.0002641502,0.0002073154,0.0001030238,0.00000255454,0.0001084324,0.00005852971,0.00001989044],"category_scores_gemma":[0.001157051,0.00004207428,0.00005454728,0.00008000014,0.0002048055,0.00006281539,0.0001171109,0.0003242432,0.000001270829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006116978,"about_ca_system_score_gemma":0.0003014885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009605826,"about_ca_topic_score_gemma":0.00002574219,"domain_scores_codex":[0.998316,0.000404065,0.0005806688,0.00008276596,0.0004408997,0.0001755493],"domain_scores_gemma":[0.9976746,0.001552267,0.0002449825,0.00003911979,0.0003757513,0.0001132108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004318664,0.00000972415,0.9724499,0.00008426054,0.0002642559,0.0001208609,0.002451124,0.0002532739,0.00004941998,0.003781735,0.01339846,0.006705094],"study_design_scores_gemma":[0.0007036058,0.00005064989,0.9707121,0.0001110511,0.00001462306,0.00006687545,0.001550537,0.0003847684,2.752678e-7,0.004062912,0.02230772,0.00003482816],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9654092,0.0008052589,0.00002356274,0.03199088,0.0006060615,0.00007003619,0.00007597638,0.000004913188,0.001014149],"genre_scores_gemma":[0.9967647,0.000664992,0.00002234552,0.00113978,0.001159993,0.00001334236,0.000009351502,0.000004153048,0.0002213491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03135553,"threshold_uncertainty_score":0.1715739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2178908896807027,"score_gpt":0.4802741454513706,"score_spread":0.262383255770668,"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."}}