{"id":"W1987457291","doi":"10.1016/j.prevetmed.2014.04.001","title":"Syndromic surveillance using laboratory test requests: A practical guide informed by experience with two systems","year":2014,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Prince Edward Island","funders":"Jordbruksverket","keywords":"Computer science; Software; Test (biology); Disease surveillance; Identification (biology); Warning system; Set (abstract data type); Epidemiological surveillance; Data science; Data mining; Disease; Medicine; Epidemiology; Pathology; Telecommunications","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"],"consensus_categories":[],"category_scores_codex":[0.001201655,0.0004614205,0.0009815872,0.0001694485,0.0001352025,0.00003411717,0.0002553799,0.00009713126,0.0001588426],"category_scores_gemma":[0.003887266,0.0003506639,0.00006227976,0.0005792421,0.0007093466,0.0005163997,0.0001562622,0.0003424864,0.00005374702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003623698,"about_ca_system_score_gemma":0.000447773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002805318,"about_ca_topic_score_gemma":0.00001347839,"domain_scores_codex":[0.9964675,0.0005343942,0.0008282398,0.0007414573,0.0008132315,0.0006151746],"domain_scores_gemma":[0.996447,0.001041802,0.000478618,0.001028106,0.000470067,0.0005344671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005036452,0.001839787,0.5435202,0.003641023,0.0008667923,0.005041272,0.002294,0.00005812082,0.3716914,0.0003910815,0.06351485,0.002105016],"study_design_scores_gemma":[0.03107232,0.02857539,0.2152731,0.01546852,0.0008007365,0.01876523,0.003850671,0.01862644,0.002054635,0.00004634716,0.6624408,0.003025813],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871515,0.001333051,0.005590063,0.0005837544,0.0004857284,0.001236622,0.0002528795,0.0003233962,0.003042981],"genre_scores_gemma":[0.995074,0.0001407319,0.002759129,0.0005057749,0.00039154,0.0001483143,0.0002815133,0.00007775517,0.0006212128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5989259,"threshold_uncertainty_score":0.9998946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405825521272772,"score_gpt":0.375763750105562,"score_spread":0.3417054948928343,"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."}}