{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002243908,0.0003444462,0.001104483,0.0001877919,0.000186772,0.00008166218,0.0006821097,0.0002432964,0.0001567639],"category_scores_gemma":[0.02349636,0.0003234622,0.0003652402,0.0005329411,0.0003798782,0.0003185022,0.0010003,0.0006066372,0.0009862868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205246,"about_ca_system_score_gemma":0.0004270906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015124,"about_ca_topic_score_gemma":0.00004307183,"domain_scores_codex":[0.9952092,0.0009029898,0.001472713,0.001215891,0.0005346366,0.00066457],"domain_scores_gemma":[0.9913388,0.003030978,0.0002511336,0.003222229,0.0003895896,0.001767264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002580228,0.001008157,0.9430379,0.0001325792,0.0001845603,0.00003906895,0.000009682789,0.000272007,0.00002893665,0.00007317911,0.02411098,0.03084498],"study_design_scores_gemma":[0.002294967,0.0006034959,0.9108258,0.0002991165,0.0005290158,0.00001191253,0.00002988959,0.01286232,0.000008798627,0.0002889652,0.07179599,0.0004497221],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897516,0.00057824,0.003699191,0.0005224774,0.001478111,0.0008670804,0.001400129,0.0006993838,0.001003788],"genre_scores_gemma":[0.9779866,0.0001542643,0.001038846,0.01667997,0.003340945,0.00002397855,0.0005991931,0.00009750825,0.00007871904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04768502,"threshold_uncertainty_score":0.9999217,"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."}}