{"id":"W4294011699","doi":"10.1093/jamia/ocac160","title":"PAN-cODE: COVID-19 forecasting using conditional latent ODEs","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"National Cancer Institute; Ontario Institute for Cancer Research; Memorial Sloan-Kettering Cancer Center","keywords":"Code (set theory); Pandemic; Computer science; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Machine learning; Econometrics; Deep learning; Economics; Medicine; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007691229,0.0005871363,0.0005601585,0.0005544884,0.0002972018,0.0007675407,0.001031261,0.0009516203,0.004578891],"category_scores_gemma":[0.004304063,0.0003700382,0.0007704789,0.0005065877,0.0003223769,0.0009095256,0.0009791944,0.001644266,0.0005845493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511472,"about_ca_system_score_gemma":0.001520623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04589135,"about_ca_topic_score_gemma":0.05166336,"domain_scores_codex":[0.9998075,0.00005274476,0.00001414384,0.00005952362,0.00003212817,0.00003385239],"domain_scores_gemma":[0.9989524,0.0006111928,0.0001022449,0.00008275241,0.0001815453,0.000069725],"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":[0.00005308702,0.0000333447,0.007101349,0.00003438612,0.00003214745,0.00005029462,0.00002853734,0.9644361,0.0003403031,0.005093999,0.004882201,0.01791424],"study_design_scores_gemma":[0.000003216895,0.000002357839,0.0001925384,0.000002771804,0.000001512025,0.000002902915,0.000002629357,0.9979791,0.00005925203,0.001424823,0.0003266061,0.000002274746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2041624,0.0007839767,0.7648274,0.00339608,0.0004682929,0.0001168962,0.01325418,0.004688566,0.008302114],"genre_scores_gemma":[0.8973805,0.0003490123,0.0874056,0.000443186,0.0001325764,0.0001182945,0.00883504,0.0002602735,0.005075538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04589135,"threshold_uncertainty_score":0.09124845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2639826309390281,"score_gpt":0.4381007259146337,"score_spread":0.1741180949756056,"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."}}