{"id":"W2118667024","doi":"10.1186/1471-2458-12-166","title":"Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions","year":2012,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Association des Médecins d'Urgence du Québec; McGill University","funders":"Canadian Institutes of Health Research; McGill University Health Centre; McGill University","keywords":"Medicine; Biostatistics; Public health; Epidemiology; Family medicine; Medical emergency; Pediatrics; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099282,0.0001691627,0.0004331784,0.00009429562,0.0002150965,0.00003456726,0.00009916569,0.00004769208,0.00003093501],"category_scores_gemma":[0.001353394,0.0001224572,0.00007161056,0.0002966057,0.0001745921,0.0002923519,0.00009189772,0.0001944046,0.00002102139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110777,"about_ca_system_score_gemma":0.0007316165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002313088,"about_ca_topic_score_gemma":0.00008512394,"domain_scores_codex":[0.9980661,0.0002909788,0.0005706499,0.0002308866,0.0002762699,0.0005650691],"domain_scores_gemma":[0.9976471,0.0006216106,0.0004515977,0.0006204586,0.0001769493,0.0004822791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007456134,0.0009566227,0.9477838,0.00133676,0.000106511,0.0000632653,0.001858424,9.59781e-7,0.00009240626,0.001621995,0.005112777,0.04099196],"study_design_scores_gemma":[0.0009437638,0.0002722028,0.9556423,0.0002096112,0.00003054246,0.001724751,0.0008524828,0.0007415899,0.000013414,0.0000359731,0.03930936,0.0002240373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930651,0.001303965,0.0005452395,0.001407367,0.0003011313,0.0006381634,0.002236767,0.00008824642,0.0004139727],"genre_scores_gemma":[0.9966285,0.0006076203,0.0005238427,0.001500452,0.0002233275,0.00004259951,0.0004382413,0.00002730282,0.000008089298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04076792,"threshold_uncertainty_score":0.4993659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07122645553676725,"score_gpt":0.3137029240692712,"score_spread":0.242476468532504,"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."}}