{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003169213,0.0001829013,0.0006575361,0.0009935609,0.00009753307,0.000007148691,0.0002398502,0.0002338061,0.0004587313],"category_scores_gemma":[0.001567605,0.0001435699,0.00005812388,0.0001574905,0.0001576593,0.0002156146,0.00010133,0.0006273927,0.000009475008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002092932,"about_ca_system_score_gemma":0.0001606997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006598187,"about_ca_topic_score_gemma":0.01206749,"domain_scores_codex":[0.9958164,0.001527883,0.001740049,0.0002590499,0.0002401745,0.0004164244],"domain_scores_gemma":[0.9953626,0.003024023,0.0009282509,0.0001220026,0.0003656372,0.0001974513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007170531,0.00003132262,0.9803686,0.0001354086,0.00002393067,0.00009470299,0.0006030221,0.0000162161,0.00001885411,0.002456532,0.0004399275,0.01509446],"study_design_scores_gemma":[0.001671155,0.0003066209,0.9790175,0.0007752627,0.000003995996,0.0001112413,0.0001239476,0.001167606,0.00001543696,0.0100442,0.006633022,0.0001299908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843038,0.0008989663,0.0001175573,0.01269771,0.001456624,0.000132173,0.0001325799,0.00001246177,0.0002481232],"genre_scores_gemma":[0.9933164,0.0004728782,0.0006688666,0.004200055,0.000854052,0.00001315237,0.00003170905,0.00001815674,0.0004247158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01496447,"threshold_uncertainty_score":0.9974529,"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."}}