{"id":"W2615868739","doi":"10.2105/ajph.2017.303767","title":"Use of a Digital Health Application for Influenza Surveillance in China","year":2017,"lang":"en","type":"article","venue":"American Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute on Drug Abuse; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"China; Medicine; Environmental health; Outbreak; Commission; Disease surveillance; People's Republic; Family medicine; Public health; Geography; Business; Virology; Nursing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00178898,0.000365739,0.0002701939,0.00195002,0.0004076619,0.0008275337,0.000643969,0.0002916891,0.001488144],"category_scores_gemma":[0.00536218,0.0001778399,0.0004428899,0.002421501,0.0002958933,0.000952384,0.0008670126,0.0001841879,0.000211116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536018,"about_ca_system_score_gemma":0.00208134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03994139,"about_ca_topic_score_gemma":0.02798448,"domain_scores_codex":[0.9991149,0.0002152468,0.0001485695,0.0001617448,0.0002664286,0.00009306425],"domain_scores_gemma":[0.997253,0.0009073265,0.0008128383,0.0002359445,0.0005400267,0.0002508552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001539686,0.0001413629,0.920661,0.0004642608,0.00008813051,0.0003894191,0.0008719289,0.000926186,0.001693827,0.0002772915,0.001673171,0.07265947],"study_design_scores_gemma":[0.00004004851,0.0002874246,0.9719997,0.0001685572,0.0002462168,0.0002915194,0.001222392,0.01572727,0.002734408,0.00018554,0.007054954,0.00004193719],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933372,0.0007792242,0.0009322082,0.000357171,0.00001438197,0.0001657055,0.002241578,0.00008366531,0.002088995],"genre_scores_gemma":[0.9956852,0.0004172881,0.001702161,0.0001132297,0.00001254392,0.00006127227,0.001504578,0.000004960856,0.0004986852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03994139,"threshold_uncertainty_score":0.07941782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07744805167144073,"score_gpt":0.3904357768784743,"score_spread":0.3129877252070336,"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."}}