{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0385819,0.003515793,0.002307171,0.004972729,0.00394631,0.009766747,0.006581741,0.009629336,0.01652635],"category_scores_gemma":[0.04272945,0.001891238,0.001722124,0.00177972,0.003200291,0.009483254,0.00968244,0.00858398,0.01482173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003551913,"about_ca_system_score_gemma":0.01205348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006175983,"about_ca_topic_score_gemma":0.01524926,"domain_scores_codex":[0.9811395,0.010862,0.003114072,0.001217758,0.002917978,0.0007487196],"domain_scores_gemma":[0.9666469,0.01885585,0.001816989,0.002148859,0.007070196,0.003461188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000376829,0.002183072,0.02163212,0.002290361,0.0002008639,0.009206585,0.01768047,0.01266364,0.006463899,0.0304271,0.2552229,0.6416522],"study_design_scores_gemma":[0.0007746607,0.002139136,0.01760005,0.01204238,0.0002244153,0.02187407,0.03711972,0.04437998,0.007575713,0.1825771,0.6726831,0.001009663],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008943446,0.004666907,0.8222229,0.09852119,0.001236823,0.007839546,0.001292228,0.009203507,0.0460734],"genre_scores_gemma":[0.02895479,0.003653741,0.9471505,0.006399698,0.000478014,0.003572661,0.0005137115,0.0006637517,0.008613061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0385819,"threshold_uncertainty_score":0.2040431,"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."}}