{"id":"W3103641981","doi":"10.1101/2020.11.16.20231399","title":"Simple Accurate Regression-Based Forecasting of Intensive Care Unit Admissions due to COVID-19 in Ontario, Canada","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intensive care unit; Medicine; Incidence (geometry); Covariate; Pandemic; Negative binomial distribution; Demography; Emergency medicine; Coronavirus disease 2019 (COVID-19); Population; Intensive care; Logistic regression; Epidemiology; Statistics; Intensive care medicine; Environmental health; Internal medicine; Disease; Mathematics; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104622,0.0004105541,0.0003729828,0.0006949377,0.0004987707,0.0006904293,0.001035516,0.0004351514,0.003104268],"category_scores_gemma":[0.007024636,0.0002638821,0.0002999333,0.0007675209,0.0003585327,0.0003524163,0.0003387254,0.0003918109,0.0004246216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01225133,"about_ca_system_score_gemma":0.009513243,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9814395,"about_ca_topic_score_gemma":0.9636677,"domain_scores_codex":[0.999701,0.00006837396,0.00001650554,0.00008480176,0.00004615854,0.00008322892],"domain_scores_gemma":[0.9982924,0.0006358256,0.0002357381,0.0000810597,0.0006425387,0.0001124322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002984206,0.00007504658,0.3343832,0.00009155498,0.00009352514,0.000232175,0.0002006777,0.6184974,0.0008324925,0.004603893,0.009199698,0.03149201],"study_design_scores_gemma":[0.00001934593,0.00001541755,0.06592795,0.00001625735,0.0000121303,0.00001618576,0.0001363727,0.9311845,0.0001757416,0.0007936824,0.001686992,0.00001533586],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612677,0.0004700411,0.01894883,0.001624158,0.00005066079,0.0001063367,0.01108851,0.000354144,0.006089624],"genre_scores_gemma":[0.9910439,0.0001459578,0.003524299,0.00003789366,0.00001448509,0.0000168096,0.002642114,0.00001798265,0.002556447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01856047,"threshold_uncertainty_score":0.08889008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4338754501019281,"score_gpt":0.4365966151262152,"score_spread":0.002721165024287042,"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."}}