{"id":"W4387568217","doi":"10.6004/jnccn.2023.7046","title":"Machine Learning–Based Early Warning Systems for Acute Care Utilization During Systemic Therapy for Cancer","year":2023,"lang":"en","type":"article","venue":"Journal of the National Comprehensive Cancer Network","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Institute for Clinical Evaluative Sciences; Vector Institute; Princess Margaret Cancer Centre; University Health Network; Ontario Institute for Cancer Research","funders":"Ipsen; Eisai; Incyte; Princess Margaret Cancer Foundation; Pfizer; University of Toronto; Exelixis; Eli Lilly and Company","keywords":"Medicine; Cohort; Acute care; Emergency department; Emergency medicine; Regimen; Retrospective cohort study; Population; Cancer; Intensive care medicine; Health care; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001512725,0.0001671826,0.0004124868,0.000127908,0.0003626551,0.00003902271,0.0001249,0.00007319831,0.00001124194],"category_scores_gemma":[0.00007683921,0.0001093446,0.000349787,0.0003749182,0.00002186082,0.00006129884,0.00001874393,0.0001884538,0.000001040676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006696317,"about_ca_system_score_gemma":0.000322563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003610571,"about_ca_topic_score_gemma":0.000005692802,"domain_scores_codex":[0.9985526,0.00008533512,0.0004256224,0.0001650043,0.0005353931,0.0002360553],"domain_scores_gemma":[0.9958441,0.0004988781,0.0006340833,0.00007896439,0.002873106,0.0000708892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001401938,0.00003896473,0.09291537,0.0003185994,0.001469842,0.000004795476,0.0004004096,0.8944991,0.0008783469,0.0000586072,0.00704695,0.0009671129],"study_design_scores_gemma":[0.02755877,0.002005847,0.6108177,0.00872638,0.002300954,0.0001726925,0.0009241283,0.2157965,0.003259732,0.0002052297,0.1276404,0.0005916833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8895902,0.1021985,0.0004680716,0.002446501,0.003008598,0.002030218,0.0001899471,0.00005146424,0.00001651609],"genre_scores_gemma":[0.9897898,0.006503951,0.00009181517,0.0005352763,0.002209422,0.0005198354,0.00005411169,0.00004461874,0.0002511088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6787025,"threshold_uncertainty_score":0.4458944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1397269178002364,"score_gpt":0.4006415005604206,"score_spread":0.2609145827601842,"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."}}