{"id":"W3120882627","doi":"10.1186/s12879-020-05754-5","title":"Monitoring sick leave data for early detection of influenza outbreaks","year":2021,"lang":"en","type":"article","venue":"BMC Infectious Diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Association Nationale de la Recherche et de la Technologie; Agence Nationale de la Recherche; Institut National de la Santé et de la Recherche Médicale; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Sick leave; Outbreak; Medicine; Absenteeism; Emergency medicine; Virology; Physical therapy","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":[0.003620198,0.0006155825,0.0004970097,0.002935812,0.0002672182,0.0009508953,0.0006515761,0.0004418706,0.0006421442],"category_scores_gemma":[0.009360488,0.0001967478,0.0006448562,0.0009311519,0.0001411808,0.0005653598,0.0005950474,0.0003764478,0.0003055368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008536974,"about_ca_system_score_gemma":0.001026762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01972939,"about_ca_topic_score_gemma":0.02660106,"domain_scores_codex":[0.9984316,0.0006980976,0.0001350164,0.0003437217,0.0002796063,0.000111972],"domain_scores_gemma":[0.9928893,0.002907323,0.002084817,0.0004616141,0.001297938,0.0003590018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002930754,0.0001329851,0.9312786,0.0003132699,0.0002729799,0.00006159626,0.0002449323,0.01025159,0.002763635,0.0002128275,0.002868973,0.05130543],"study_design_scores_gemma":[0.00006228592,0.0005373765,0.786283,0.0001966996,0.0002674406,0.0001814176,0.0003999681,0.1996401,0.006728831,0.0008032914,0.004839369,0.00006014106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425625,0.00246228,0.03791299,0.0008626858,0.0001026511,0.0002386131,0.01314318,0.0008818733,0.001833202],"genre_scores_gemma":[0.9806584,0.0002795165,0.01255662,0.0001033644,0.00006154103,0.00007332676,0.006001662,0.00001622562,0.0002493442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01972939,"threshold_uncertainty_score":0.03922909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05194151012643901,"score_gpt":0.3326694082983195,"score_spread":0.2807278981718805,"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."}}