{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002545892,0.000322041,0.0008044464,0.0004734434,0.0007419048,0.0001297599,0.0001605447,0.0000747297,0.00002794822],"category_scores_gemma":[0.0002511725,0.0003388014,0.00005701204,0.001055752,0.000105876,0.0002950261,0.0003732104,0.0004977938,0.000007913279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009168184,"about_ca_system_score_gemma":0.001774353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000081476,"about_ca_topic_score_gemma":0.0004025197,"domain_scores_codex":[0.9961934,0.0006422672,0.0008584555,0.0008037608,0.0005454356,0.0009566658],"domain_scores_gemma":[0.9978883,0.000233398,0.0002894858,0.0004614296,0.0001160424,0.001011316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009350522,0.0001505849,0.9783313,0.0007905263,0.00006254641,0.0001103324,0.0005940677,0.000006222895,0.00001441699,0.0003963606,0.002365016,0.01708509],"study_design_scores_gemma":[0.001626161,0.0002279442,0.6840889,0.00005216394,7.888691e-7,0.0003443985,0.0007876477,0.0002323302,3.901136e-7,0.00000490333,0.3123311,0.0003031895],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886805,0.003872721,0.0000886072,0.00260971,0.0003117993,0.001304938,0.000406751,0.0004046368,0.002320356],"genre_scores_gemma":[0.9963841,0.000194241,0.0001639458,0.002059754,0.00008594838,0.0005491229,0.0002981053,0.00004949843,0.0002152848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3099661,"threshold_uncertainty_score":0.9999064,"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."}}