{"id":"W2809342310","doi":"10.1093/ije/dyy106","title":"Low birthweight and preterm birth: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982–2015","year":2018,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Maternal and Neonatal Healthcare","field":"Health Professions","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministério da Saúde; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Associação Brasileira de Saúde Coletiva; European Commission; International Development Research Centre; Wellcome Trust; World Health Organization","keywords":"Medicine; Demography; Population; Inequality; Premature birth; Low birth weight; Obstetrics; Pediatrics; Pregnancy; Environmental health; Gestational age; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001183037,0.0003291483,0.0004729313,0.001479734,0.0005620104,0.0008056007,0.0005775102,0.0003469573,0.0008033331],"category_scores_gemma":[0.003264916,0.0004946523,0.0008912215,0.002427833,0.0003802015,0.0004766222,0.001158818,0.0004538852,0.0001442387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001498503,"about_ca_system_score_gemma":0.001397763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2826141,"about_ca_topic_score_gemma":0.2851078,"domain_scores_codex":[0.9994998,0.00008352438,0.00007746313,0.0001424556,0.00009060003,0.0001061942],"domain_scores_gemma":[0.9986889,0.0001444024,0.0005006356,0.0001298562,0.0003526001,0.0001836183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004797654,0.00002091932,0.9954957,0.00008254853,0.0001376682,0.00008683644,0.00104782,0.0000452016,0.0003383757,0.0001038805,0.0002092133,0.0023838],"study_design_scores_gemma":[0.000002780454,0.00001644595,0.9987038,0.00003439821,0.00004259234,0.00007009235,0.0006177499,0.00006042108,0.00003354499,0.00002225096,0.0003913877,0.000004461657],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936892,0.001525221,0.0002168546,0.0002155938,0.00001151497,0.00004401066,0.003535508,0.00001076319,0.000751415],"genre_scores_gemma":[0.9972072,0.0008223429,0.0002104934,0.00004298512,0.000004584318,0.00004228493,0.001441478,0.000004516693,0.0002239064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2826141,"threshold_uncertainty_score":0.5619383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08848234979912797,"score_gpt":0.4557724152381285,"score_spread":0.3672900654390006,"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."}}