{"id":"W3085113106","doi":"10.15586/jptcp.v27isp1.721","title":"An application of a mixture of exponential distributions for assessing hazard rates from COVID-19","year":2020,"lang":"en","type":"article","venue":"Journal of Population Therapeutics and Clinical Pharmacology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hazard; Statistics; Exponential function; Coronavirus disease 2019 (COVID-19); Exponential distribution; Hazard ratio; Mathematics; Econometrics; Computer science; Disease; Medicine; Infectious disease (medical specialty); Biology; Confidence interval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.02050301,0.001236568,0.001293393,0.004165374,0.0006802389,0.002002463,0.002183749,0.002056694,0.002969882],"category_scores_gemma":[0.05219636,0.0006337194,0.002459658,0.002247206,0.0009483438,0.002923137,0.002141509,0.00265386,0.0006473545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050058,"about_ca_system_score_gemma":0.001247967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004231214,"about_ca_topic_score_gemma":0.001836113,"domain_scores_codex":[0.9943176,0.003233901,0.0003048225,0.0009467881,0.0009400327,0.0002567473],"domain_scores_gemma":[0.9773088,0.01793919,0.001838087,0.001297028,0.001284505,0.0003324437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001741811,0.0004725885,0.08302668,0.000627305,0.001110474,0.0007677148,0.001132576,0.484684,0.007419734,0.1711289,0.003569434,0.2443188],"study_design_scores_gemma":[0.00005986363,0.0004926181,0.01146887,0.0001119723,0.0001875266,0.0007077405,0.0002468206,0.9051726,0.001505117,0.0750387,0.004862766,0.0001452398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03515099,0.0004510068,0.9622715,0.0002440675,0.00005821739,0.0002176912,0.0003106415,0.0002575886,0.001038256],"genre_scores_gemma":[0.5738237,0.0009495942,0.4188545,0.000314887,0.0001500373,0.001086938,0.001355783,0.0001848015,0.003279854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02050301,"threshold_uncertainty_score":0.1084316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.415654305285768,"score_gpt":0.5867281123041647,"score_spread":0.1710738070183966,"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."}}