{"id":"W4309157948","doi":"10.2196/39231","title":"Accuracy of COVID-19–Like Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Medicine; Emergency department; Retrospective cohort study; Diagnosis code; Medical diagnosis; Cohort study; Cohort; Observational study; Electronic health record; Epidemiology; Emergency medicine; MEDLINE; Pediatrics; Health care; Internal medicine; Psychiatry; Population; Pathology","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":[0.008643635,0.0004959612,0.0007032989,0.002466147,0.0006635484,0.00173742,0.001286593,0.0007731885,0.001211474],"category_scores_gemma":[0.02952637,0.0008335699,0.001408471,0.00332924,0.0006345069,0.001773091,0.001571044,0.001017718,0.0003792451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008400585,"about_ca_system_score_gemma":0.0008897637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017238,"about_ca_topic_score_gemma":0.01009866,"domain_scores_codex":[0.9897054,0.002944445,0.001875135,0.003008564,0.00171758,0.0007489909],"domain_scores_gemma":[0.964332,0.008258387,0.017552,0.005730156,0.003298185,0.0008290928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006203767,0.00002128955,0.999313,0.000009121849,0.00007894336,0.0000168906,0.00003471119,0.00003471979,0.00003590924,0.00001524833,0.00006454867,0.0003136344],"study_design_scores_gemma":[0.00002030623,0.0001130911,0.9976016,0.00003155841,0.0001009999,0.0002626926,0.0002033836,0.001214915,0.0001023532,0.000043211,0.0002944493,0.00001147152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995615,0.0002912827,0.0005540572,0.00006594811,0.00002049127,0.00005939596,0.003069009,0.00001283109,0.0003119998],"genre_scores_gemma":[0.9968453,0.0001190688,0.0004517981,0.00005325245,0.00001706572,0.00004828756,0.002394895,0.000006990852,0.0000632917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01017238,"threshold_uncertainty_score":0.04571247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08834359484281383,"score_gpt":0.4843026038609434,"score_spread":0.3959590090181295,"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."}}