{"id":"W2133154607","doi":"10.1017/s1481803500009969","title":"Can Telehealth Ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health?","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Queen's University; Ontario Ministry of Health and Long-Term Care","keywords":"Telehealth; Medicine; Emergency department; Proxy (statistics); Medical emergency; Emergency medicine; Medical diagnosis; Telemedicine; Diagnosis code; Public health; Health care; Environmental health; Statistics; Nursing","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003045959,0.000567245,0.001554504,0.0007845062,0.0006180069,0.000008267752,0.0006123985,0.0002013007,0.006045042],"category_scores_gemma":[0.002344602,0.0005108606,0.0003823478,0.0008717144,0.0002993256,0.0002461464,0.00002812316,0.0007726772,0.00002693817],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002876492,"about_ca_system_score_gemma":0.02735969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1315761,"about_ca_topic_score_gemma":0.7780316,"domain_scores_codex":[0.9931723,0.0004227805,0.002855309,0.0006784604,0.001236222,0.001634968],"domain_scores_gemma":[0.9893622,0.0000759297,0.001486342,0.0009412233,0.001666897,0.006467379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001250721,0.00009904011,0.4347768,0.0001706121,0.0001802701,0.0002651349,0.001411993,0.000001828178,0.000123236,0.00002890922,0.5623004,0.0005167357],"study_design_scores_gemma":[0.002762244,0.003488615,0.2124502,0.0001489033,0.00005521797,0.00009759051,0.0001583622,0.00000848775,0.00001152538,0.00003456523,0.7804533,0.0003310542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8807765,0.03115085,0.000268027,0.07188738,0.009329599,0.003036529,0.001632094,0.00008518933,0.0018338],"genre_scores_gemma":[0.9672369,0.001813717,0.0002602579,0.01455303,0.003472928,0.000173815,0.0008837691,0.0002375581,0.01136797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6464555,"threshold_uncertainty_score":0.9997343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.116022377992086,"score_gpt":0.3485213377527116,"score_spread":0.2324989597606256,"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."}}