{"id":"W4306168025","doi":"10.2196/37669","title":"Digitalizing and Upgrading Severe Acute Respiratory Infections Surveillance in Malta: System Development","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Centre for Disease Prevention and Control","keywords":"Medicine; Public health; Public health surveillance; Incidence (geometry); Pandemic; Medical emergency; Disease surveillance; Environmental health; Pediatrics; Epidemiology; Coronavirus disease 2019 (COVID-19); Disease; Family medicine; Emergency medicine; Internal medicine; Infectious disease (medical specialty); 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":[0.01690217,0.0004360672,0.0003041107,0.005208419,0.001299826,0.006632015,0.00218929,0.0005259928,0.002128246],"category_scores_gemma":[0.03619837,0.0004180314,0.0003754037,0.006720274,0.001529263,0.004800716,0.006006282,0.0006895682,0.0005669357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01528388,"about_ca_system_score_gemma":0.01956217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1360244,"about_ca_topic_score_gemma":0.08532813,"domain_scores_codex":[0.9895781,0.005630106,0.001283324,0.001109473,0.001386074,0.001012825],"domain_scores_gemma":[0.9672031,0.008283962,0.005927902,0.004229564,0.01169142,0.002664092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000277159,0.0003597379,0.4330222,0.000993297,0.00009391459,0.0002695656,0.007594281,0.005061121,0.00346769,0.004752665,0.01176303,0.5323454],"study_design_scores_gemma":[0.000159505,0.0009158626,0.7672035,0.00232881,0.0002141294,0.0005029808,0.02540561,0.05211246,0.01247223,0.003877925,0.1345794,0.0002276244],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8343993,0.005513622,0.051859,0.04784247,0.0002189574,0.007296558,0.01126437,0.004572697,0.037033],"genre_scores_gemma":[0.8920388,0.003877651,0.09350166,0.001034238,0.00008102709,0.001435519,0.005588441,0.0001030299,0.002339628],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1360244,"threshold_uncertainty_score":0.2704653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02979473083371505,"score_gpt":0.2991308132524663,"score_spread":0.2693360824187512,"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."}}