{"id":"W4223550323","doi":"10.2196/38505","title":"Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)","year":2022,"lang":"en","type":"erratum","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Health records; Medicine; Pharmacopoeia; 2019-20 coronavirus outbreak; Data science; Data mining; Alternative medicine; Computer science; Virology; Internal medicine; Pathology; Health care; Disease; Outbreak; Political science","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":[0.009055614,0.003279753,0.002801525,0.006244981,0.003742424,0.004977601,0.004399133,0.008632842,0.05948189],"category_scores_gemma":[0.1411043,0.001867964,0.0030243,0.005131627,0.003404832,0.002546115,0.002502111,0.0148149,0.03008941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00488791,"about_ca_system_score_gemma":0.01086614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05102991,"about_ca_topic_score_gemma":0.05053689,"domain_scores_codex":[0.9895723,0.001804758,0.002496118,0.001151831,0.0043006,0.0006744522],"domain_scores_gemma":[0.9262125,0.02493413,0.003499066,0.004205151,0.03886821,0.002280876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002383321,0.00000518903,0.0000713445,0.0001210983,0.0000173781,0.0001965777,0.00003416751,0.00003247982,0.00002130444,0.0003900739,0.9964554,0.00263113],"study_design_scores_gemma":[0.0001561863,0.0000372805,0.001297245,0.001095991,0.0001196438,0.001048772,0.0001788044,0.0005026245,0.0003138245,0.002250822,0.9929169,0.00008179436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001729848,0.0007375558,0.0009299177,0.0818686,0.9094109,0.0000472842,0.004289111,0.0006062342,0.00193743],"genre_scores_gemma":[0.01753208,0.01036131,0.01122475,0.2587895,0.5048593,0.0005897268,0.009458209,0.003021315,0.1841638],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05948189,"threshold_uncertainty_score":0.1989868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542061215256219,"score_gpt":0.3808498386090004,"score_spread":0.3554292264564382,"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."}}