{"id":"W4235435082","doi":"10.21203/rs.3.rs-37929/v1","title":"Number of Pre-Existing Comorbidities and Prognosis of COVID-19: A Retrospective Cohort Study","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SKiN Health","funders":"Central South University; National Natural Science Foundation of China","keywords":"Coronavirus disease 2019 (COVID-19); Retrospective cohort study; Cohort; Medicine; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Comorbidity; Internal medicine; Virology; Disease; Outbreak","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":["metaresearch","metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04340388,0.001033664,0.004359208,0.002548244,0.001142897,0.0002930026,0.00203748,0.0009428728,0.001218902],"category_scores_gemma":[0.3900509,0.0009267638,0.0008362888,0.004524861,0.008095471,0.0002272657,0.01597052,0.01230639,0.00007559445],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003389166,"about_ca_system_score_gemma":0.01249727,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02528701,"about_ca_topic_score_gemma":0.0005838373,"domain_scores_codex":[0.9623321,0.009723687,0.002945163,0.003804908,0.01801613,0.003177964],"domain_scores_gemma":[0.9189389,0.05551212,0.0008848217,0.0036215,0.01713395,0.003908653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004297548,0.0044555,0.9213462,0.04494898,0.002325291,0.0008117213,0.01118172,0.00004818029,0.0003296463,0.001263762,0.007733122,0.001258353],"study_design_scores_gemma":[0.004840717,0.01124099,0.9453304,0.005054775,0.000349368,0.00001194487,0.01361497,0.001789244,0.0007564743,0.01271131,0.003493696,0.0008061003],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9433598,0.001687785,0.000318706,0.01756056,0.0001875587,0.03012884,0.001543693,0.0003271767,0.004885908],"genre_scores_gemma":[0.9867432,0.00559917,0.0006659011,0.0001088387,0.0006588848,0.004573147,0.000204219,0.0002296581,0.001216972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.346647,"threshold_uncertainty_score":0.9996941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2591300841335013,"score_gpt":0.5642586243433163,"score_spread":0.305128540209815,"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."}}