{"id":"W4386961589","doi":"10.1111/irv.13201","title":"Lessons learned from identifying clusters of severe acute respiratory infections with influenza sentinel surveillance, Bangladesh, 2009–2020","year":2023,"lang":"en","type":"article","venue":"Influenza and Other Respiratory Viruses","topic":"Respiratory viral infections research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research","funders":"Centers for Disease Control and Prevention","keywords":"Outbreak; Human metapneumovirus; Medicine; Cluster (spacecraft); Virology; Metapneumovirus; Virus; Influenza A virus; Influenza A virus subtype H5N1; Respiratory system; Respiratory tract infections; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009571272,0.0005316833,0.0008612655,0.001071411,0.0004517862,0.0001795638,0.0002809158,0.0003638004,0.0003611308],"category_scores_gemma":[0.0004175349,0.0004601192,0.0002161172,0.002321532,0.0007064285,0.0005396041,0.0002725961,0.0007439859,0.0003877992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001209627,"about_ca_system_score_gemma":0.0004592168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005292056,"about_ca_topic_score_gemma":0.000507345,"domain_scores_codex":[0.9959577,0.0004681976,0.001043637,0.0009191199,0.0008612815,0.0007501059],"domain_scores_gemma":[0.9972486,0.0004296871,0.0004296849,0.001059114,0.0003999374,0.0004329878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008835249,0.000169622,0.9503328,0.0002006397,0.0007716939,0.0001730535,0.0004205721,0.0003221161,0.04133019,0.00009369441,0.003725409,0.001576741],"study_design_scores_gemma":[0.005006418,0.0007535915,0.3117517,0.0003762038,0.0002188937,0.00002880734,0.0003518547,0.00004847154,0.005923423,0.0001426824,0.674808,0.00058991],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946851,0.001305683,0.00005355209,0.00006439174,0.0001435402,0.0007391428,0.0003901435,0.0005389473,0.002079478],"genre_scores_gemma":[0.8324292,0.00008591772,0.0002085806,0.1644778,0.001162092,0.000580846,0.00001581641,0.0006592606,0.0003805054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6710826,"threshold_uncertainty_score":0.9997851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2267364905190948,"score_gpt":0.4348173031409965,"score_spread":0.2080808126219017,"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."}}