{"id":"W4388592714","doi":"10.51161/conbrasp2023/27663","title":"IMPACTOS DA COVID 19 NA ATENÇÃO PRIMÁRIA: ANÁLISE DOS INDICADORES DE SAÚDE DO MUNICÍPIO DE DIAS D’ÁVILA NO PREVINE BRASIL","year":2023,"lang":"pt","type":"article","venue":"","topic":"Healthcare during COVID-19 Pandemic","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006128776,0.001075311,0.001211043,0.001143303,0.0009715593,0.001266491,0.00519499,0.001028694,0.0008051074],"category_scores_gemma":[0.007529928,0.001028036,0.0004737902,0.003721421,0.0004266646,0.001099409,0.003157174,0.001944179,0.003532236],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002821414,"about_ca_system_score_gemma":0.01087795,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00898081,"about_ca_topic_score_gemma":0.0008187845,"domain_scores_codex":[0.988972,0.001982688,0.001616028,0.002169344,0.001629279,0.003630634],"domain_scores_gemma":[0.9887242,0.002867058,0.0006641421,0.003463349,0.0003004238,0.003980883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006105027,0.001040396,0.6627116,0.00721659,0.0007758185,0.004198985,0.06226021,0.004173889,0.007492038,0.006423411,0.1940018,0.04909479],"study_design_scores_gemma":[0.01003825,0.002462259,0.473509,0.002696784,0.0003881964,0.001427593,0.00136471,0.3417959,0.003575527,0.002893519,0.1541999,0.00564833],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8982599,0.00282037,0.05999092,0.02583891,0.002504918,0.003337649,0.0002714284,0.00436822,0.002607713],"genre_scores_gemma":[0.9695244,0.002243862,0.003359134,0.01784777,0.0007666217,0.0001648916,0.00005815744,0.0001738315,0.005861267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.337622,"threshold_uncertainty_score":0.9997703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06709942250809017,"score_gpt":0.3728348650959153,"score_spread":0.3057354425878251,"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."}}