{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002275266,0.0005020658,0.001249928,0.0005244414,0.0004233,0.00001178344,0.001382562,0.0001934629,0.002279259],"category_scores_gemma":[0.002542224,0.0004699441,0.0001903627,0.000924808,0.0001961566,0.0002393446,0.004241418,0.001460997,0.0001048668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004600803,"about_ca_system_score_gemma":0.0003623118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008314826,"about_ca_topic_score_gemma":0.0007218531,"domain_scores_codex":[0.9940174,0.001504085,0.001620144,0.001329263,0.0005203025,0.001008781],"domain_scores_gemma":[0.9957989,0.0004537011,0.0004424666,0.002582941,0.0003019637,0.0004200687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006295674,0.0009725009,0.8886231,0.00007456821,0.0002605029,0.0001963636,0.001695218,0.001626273,0.00007552424,0.000258199,0.08826394,0.01732419],"study_design_scores_gemma":[0.004193691,0.004677716,0.6881178,0.000036398,0.0002126656,0.0003533869,0.004947972,0.2068937,0.000004646734,0.0003725351,0.08933054,0.0008590205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990846,0.0006572686,0.0009336196,0.002094964,0.0008381158,0.001420929,0.0007949314,0.0004292098,0.001985012],"genre_scores_gemma":[0.9854823,0.00004817608,0.00318151,0.001480607,0.0003177459,0.001440107,0.006928003,0.00007043881,0.00105106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2052674,"threshold_uncertainty_score":0.9997752,"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."}}