{"id":"W3016656771","doi":"10.1101/2020.04.13.20059691","title":"International Electronic Health Record-Derived COVID-19 Clinical Course Profiles: The 4CE Consortium","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Informatics; Coronavirus disease 2019 (COVID-19); Observational study; Psychological intervention; Data science; Health informatics; Electronic health record; General partnership; Aggregate data; Download; Health records; Computer science; Medicine; Disease; Public health; Infectious disease (medical specialty); Business; Political science; World Wide Web; Health care; Pathology; Nursing","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":[0.05986925,0.0004239072,0.0006456154,0.006079519,0.0009627311,0.004775052,0.00207361,0.0009334027,0.002974778],"category_scores_gemma":[0.1128219,0.0003934878,0.000670524,0.009939708,0.0009703578,0.002634246,0.008151093,0.001488171,0.001112497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002214381,"about_ca_system_score_gemma":0.005579219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01379437,"about_ca_topic_score_gemma":0.008677069,"domain_scores_codex":[0.9611874,0.02252322,0.003348712,0.004192132,0.006764138,0.00198446],"domain_scores_gemma":[0.8848072,0.03309361,0.01744064,0.03517484,0.02182845,0.00765538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006169458,0.0003833733,0.9307626,0.000103714,0.0001895466,0.0001306341,0.001436891,0.001780869,0.0006499861,0.002914731,0.01466805,0.04636266],"study_design_scores_gemma":[0.0005940165,0.0005313147,0.901281,0.0006596059,0.0002308111,0.0006748792,0.006067448,0.01933419,0.004532513,0.004132433,0.06180236,0.0001595009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9262887,0.0007428075,0.01441635,0.00664456,0.0001553657,0.001241667,0.03936663,0.0004216189,0.01072228],"genre_scores_gemma":[0.8873479,0.0003993383,0.02326102,0.001306466,0.0001738481,0.001662996,0.08366793,0.0003333857,0.001847166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05986925,"threshold_uncertainty_score":0.3166226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08737713892341051,"score_gpt":0.4221502579483321,"score_spread":0.3347731190249216,"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."}}