{"id":"W4234577219","doi":"10.2196/33834","title":"Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Intensive Care Unit Cognitive Disorders","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; American Academy of Sleep Medicine; James S. McDonnell Foundation; National Institutes of Health; Glenn Foundation for Medical Research; Fondation pour la Recherche Médicale","keywords":"Delirium; Receiver operating characteristic; Medicine; Artificial intelligence; Machine learning; Confusion; Confusion matrix; Medical record; Observational study; Gold standard (test); Clinical decision support system; Health records; Natural language processing; Internal medicine; Intensive care medicine; Computer science; Decision support system; Psychology; Health care","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001594423,0.0001845696,0.0003221146,0.0006157442,0.0007072838,0.00004343737,0.00023047,0.00005305663,0.000708272],"category_scores_gemma":[0.005001935,0.000145544,0.0001181786,0.001090627,0.000230275,0.0001748599,0.0002412258,0.001727523,0.00006903733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004047418,"about_ca_system_score_gemma":0.0004951222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002253573,"about_ca_topic_score_gemma":0.00001000671,"domain_scores_codex":[0.9947462,0.001133123,0.00045952,0.000394625,0.002777566,0.0004889421],"domain_scores_gemma":[0.9805365,0.001350948,0.0001241462,0.0003666292,0.0174566,0.0001651692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005827522,0.003735342,0.9665742,0.00007338113,0.0002036357,0.00007457016,0.005046442,0.00009289325,0.00001813102,0.0001733896,0.01457312,0.003607411],"study_design_scores_gemma":[0.008122643,0.01015651,0.9499866,0.00007862582,0.00002402335,0.000002362559,0.02889476,0.001655553,0.00003795316,0.00008544791,0.0007945697,0.0001608963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929122,0.000005033618,0.0003724126,0.001866793,0.000232921,0.00266353,0.0001158644,0.00004240073,0.001788883],"genre_scores_gemma":[0.989608,5.278931e-7,0.0001451442,0.008881964,0.00005484186,0.0005831788,0.0006017169,0.00003724073,0.00008737284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02384832,"threshold_uncertainty_score":0.7755082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161903191724032,"score_gpt":0.4861274371908355,"score_spread":0.2699371180184323,"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."}}