{"id":"W4224220726","doi":"10.2196/32386","title":"Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Web search query; Emergency department; Baseline (sea); Medicine; Computer science; Health care; Mean absolute error; Volume (thermodynamics); Medical emergency; Emergency medicine; Information retrieval; Data mining; Mean squared error; Search engine; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003903385,0.0006463687,0.0004941584,0.002133789,0.0002276492,0.0009236201,0.000781791,0.0006108365,0.0008434834],"category_scores_gemma":[0.01337582,0.0002607079,0.00153079,0.002861546,0.0002978772,0.001614187,0.000633011,0.0006988298,0.0003385465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323852,"about_ca_system_score_gemma":0.001433165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05344634,"about_ca_topic_score_gemma":0.03029116,"domain_scores_codex":[0.9984268,0.0007642768,0.0001327513,0.0002756702,0.0002964312,0.0001040934],"domain_scores_gemma":[0.9908879,0.005392416,0.001308119,0.0007645177,0.001300681,0.0003464171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003777581,0.0005215608,0.9672883,0.0001425744,0.0003976891,0.0001520078,0.0002239973,0.007644658,0.0001609214,0.0003122748,0.001568231,0.02121011],"study_design_scores_gemma":[0.00009420131,0.001176359,0.7770814,0.000124727,0.0005696794,0.0004193888,0.000979948,0.2149826,0.0008948762,0.000487341,0.003140897,0.00004856654],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962447,0.0004425208,0.000897259,0.0002433481,0.00001675148,0.00006303172,0.001426914,0.00004139091,0.0006241064],"genre_scores_gemma":[0.9953499,0.0002830387,0.001346311,0.00006816718,0.00005949958,0.00004245088,0.002648141,0.00001158249,0.0001910363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05344634,"threshold_uncertainty_score":0.1062705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08298358006252113,"score_gpt":0.382464308479162,"score_spread":0.2994807284166409,"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."}}