{"id":"W1558250787","doi":"10.1111/j.1750-2659.2011.00297.x","title":"Invasive bacterial infections following influenza: a time‐series analysis in Montréal, Canada, 1996–2008","year":2011,"lang":"en","type":"article","venue":"Influenza and Other Respiratory Viruses","topic":"Bacterial Infections and Vaccines","field":"Immunology and Microbiology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut National de Santé Publique du Québec; McGill University","funders":"","keywords":"Streptococcus pyogenes; Streptococcus pneumoniae; Haemophilus influenzae; Incidence (geometry); Neisseria meningitidis; Seasonal influenza; Medicine; Influenza-like illness; Microbiology; Biology; Immunology; Virology; Internal medicine; Staphylococcus aureus; Virus; Antibiotics; Coronavirus disease 2019 (COVID-19); Disease; Infectious disease (medical specialty); Bacteria","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001872755,0.0002806761,0.0004884633,0.0004398176,0.0002944412,0.00004324985,0.0001317856,0.0002445684,0.0014938],"category_scores_gemma":[0.0001313233,0.000253772,0.0001474511,0.0005798154,0.0001177657,0.0003492084,0.00008044227,0.0002015311,0.0001060727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007018314,"about_ca_system_score_gemma":0.000262486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5769293,"about_ca_topic_score_gemma":0.8696757,"domain_scores_codex":[0.9984876,0.0002285474,0.0005391992,0.0003541281,0.00002921334,0.0003613451],"domain_scores_gemma":[0.999315,0.0001056257,0.0001657177,0.000310623,0.00004973302,0.00005325862],"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":[0.0002631383,0.0001077758,0.918111,0.000008048186,0.0009048464,0.000009669872,0.0005968045,0.00000920503,0.07887333,0.0001018263,0.0008600891,0.0001542501],"study_design_scores_gemma":[0.001279168,0.0001936743,0.2465122,0.0000266783,0.0002764932,0.000009214478,0.0001406986,2.23451e-7,0.01504882,0.00003962878,0.7361498,0.0003234596],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955983,0.002601566,0.000002857564,0.000003323236,0.0005163808,0.0002004659,0.00009920309,0.00007520636,0.0009027206],"genre_scores_gemma":[0.9827815,0.000008597396,0.00003143704,0.01680483,0.0001190283,0.0001241572,0.000004012658,0.00005514926,0.00007124818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7352897,"threshold_uncertainty_score":0.9999915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05228144941408151,"score_gpt":0.277140979858821,"score_spread":0.2248595304447394,"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."}}