{"id":"W4412543146","doi":"10.1101/2025.07.20.25331858","title":"Environmental Profiles and COVID-19 Mortality Risk: A Latent Class Analysis of Intensive Care Patients in the Early Pandemic","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia College","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Latent class model; 2019-20 coronavirus outbreak; Class (philosophy); Intensive care; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Intensive care medicine; Virology; Computer science; Statistics; Internal medicine; Artificial intelligence; Mathematics; Outbreak; Disease; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002402531,0.0004414014,0.0004410877,0.001233152,0.0005657513,0.001260938,0.0005494862,0.0006391707,0.002371188],"category_scores_gemma":[0.003616219,0.0002543632,0.001395835,0.00127596,0.0004987697,0.000576759,0.001414745,0.001001811,0.0003093852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00044086,"about_ca_system_score_gemma":0.0005306739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007627131,"about_ca_topic_score_gemma":0.003975399,"domain_scores_codex":[0.9988813,0.0005126291,0.00008582715,0.0002158657,0.0000941519,0.0002101672],"domain_scores_gemma":[0.9968026,0.0008511654,0.001198276,0.0004125072,0.0002032507,0.0005323461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001927249,0.00004698089,0.9985051,0.000006044049,0.0000834883,0.00002167899,0.00007523192,0.0002320148,0.0001186154,0.00003114341,0.00008570871,0.0006012567],"study_design_scores_gemma":[0.00001348356,0.0001208986,0.993956,0.00001109958,0.00004008827,0.0000636602,0.0003672453,0.00513835,0.00004029203,0.000133139,0.0001057853,0.000009962158],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988717,0.00006950911,0.0004527479,0.0000527952,0.00000562022,0.000009676746,0.000414183,0.000003835012,0.0001198118],"genre_scores_gemma":[0.9993488,0.0000210733,0.0001310607,0.000008690481,0.000005463436,0.000008855237,0.000414128,0.000002631861,0.00005932317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007627131,"threshold_uncertainty_score":0.01516545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06821535937081757,"score_gpt":0.3365477081179258,"score_spread":0.2683323487471082,"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."}}