{"id":"W2097936813","doi":"10.1093/cid/ciu647","title":"Using Clinicians' Search Query Data to Monitor Influenza Epidemics","year":2014,"lang":"en","type":"article","venue":"Clinical Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Children's Hospital","funders":"Stanford Bio-X; U.S. National Library of Medicine; Baidu","keywords":"Medicine; Outbreak; Web search query; Data mining; Information retrieval; Database; Computer science; Virology; Search engine","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":[0.00465913,0.0005416576,0.001019513,0.004109381,0.0002894994,0.001718183,0.0008217499,0.0007127115,0.001176656],"category_scores_gemma":[0.04135545,0.0001749709,0.0004048138,0.004100112,0.000178873,0.001294102,0.0007785358,0.0004518007,0.0006460081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008977353,"about_ca_system_score_gemma":0.001277428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01752903,"about_ca_topic_score_gemma":0.02115099,"domain_scores_codex":[0.9953325,0.00208319,0.0006773439,0.0008367526,0.000916662,0.0001533978],"domain_scores_gemma":[0.9733027,0.01780261,0.003555166,0.001514568,0.002995426,0.0008294971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001995196,0.0004023074,0.8430113,0.001011865,0.0004297522,0.0002489533,0.0005419226,0.0100371,0.003413064,0.0008920559,0.01417994,0.1238365],"study_design_scores_gemma":[0.0007052464,0.002166905,0.5491397,0.0004381675,0.001000409,0.001723483,0.001875673,0.3860118,0.02094321,0.004596682,0.03116234,0.000236334],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9103999,0.004284034,0.01613215,0.003156128,0.0001843462,0.0006724801,0.05305806,0.002191583,0.009921324],"genre_scores_gemma":[0.9661469,0.0005868507,0.0148259,0.0004266809,0.00006423758,0.0001519814,0.0172505,0.00003161913,0.000515276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01752903,"threshold_uncertainty_score":0.03485394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3011561238516651,"score_gpt":0.514192655922724,"score_spread":0.2130365320710589,"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."}}