{"id":"W4405654482","doi":"10.2196/67050","title":"Impact of Primary Health Care Data Quality on Infectious Disease Surveillance in Brazil: Case Study","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Oswaldo Cruz; Universidade Federal do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Preprint; Infectious disease (medical specialty); Environmental health; Medicine; Public health surveillance; Quality (philosophy); Public health; Disease surveillance; Primary care; Data quality; Disease; Family medicine; Computer science; World Wide Web; Nursing; Business; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01687928,0.000435653,0.0004769028,0.001540105,0.001027393,0.001856747,0.001049754,0.0008395875,0.0007232243],"category_scores_gemma":[0.07636748,0.0003087581,0.0008434895,0.003702643,0.001380979,0.001095859,0.002239679,0.0008346253,0.0000708303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007074235,"about_ca_system_score_gemma":0.006247701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1740101,"about_ca_topic_score_gemma":0.08418724,"domain_scores_codex":[0.9828686,0.009997745,0.00171271,0.001185489,0.002895868,0.00133963],"domain_scores_gemma":[0.939896,0.03542152,0.01172125,0.003972745,0.007108085,0.001880328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001053946,0.0001539895,0.9635154,0.0004302994,0.0001500517,0.002136565,0.003798689,0.002241161,0.000363052,0.001328941,0.001711423,0.02406503],"study_design_scores_gemma":[0.00006346252,0.0005120577,0.9437151,0.00110562,0.0002856902,0.005699988,0.01230168,0.02479474,0.001315268,0.001541876,0.008589935,0.00007454486],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984093,0.00217097,0.003470215,0.003994674,0.00002531769,0.0004006551,0.001538774,0.00005539995,0.004250957],"genre_scores_gemma":[0.9975821,0.0004140438,0.001410772,0.0001691712,0.0000111618,0.00006192957,0.0002902272,0.000005739247,0.00005487096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1740101,"threshold_uncertainty_score":0.3459945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06219398795865693,"score_gpt":0.4340855122549407,"score_spread":0.3718915242962838,"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."}}