{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004023909,0.000628326,0.0007408465,0.001279542,0.0002941453,0.0008541637,0.0007336935,0.0005088091,0.0008971376],"category_scores_gemma":[0.01663397,0.0002418844,0.0006856296,0.0005680526,0.0001946046,0.0007592848,0.0007051995,0.0008938718,0.0002411801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008943144,"about_ca_system_score_gemma":0.001032096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003656221,"about_ca_topic_score_gemma":0.003923299,"domain_scores_codex":[0.9986077,0.0005862357,0.0001597013,0.000294004,0.0002548582,0.00009750897],"domain_scores_gemma":[0.988063,0.006486211,0.002960401,0.0005717714,0.001523654,0.0003949899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001030136,0.0008070873,0.6270178,0.0002274721,0.0004267007,0.0001432961,0.000180596,0.1829213,0.002764331,0.0006914882,0.005770802,0.1780191],"study_design_scores_gemma":[0.00008922602,0.0008214734,0.08056813,0.00007204138,0.0001705689,0.0001431256,0.00005319207,0.9125996,0.00263772,0.001489594,0.001305369,0.00005003141],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8784919,0.00118636,0.1108234,0.001931729,0.0002129736,0.0003827752,0.002663062,0.002292959,0.002014757],"genre_scores_gemma":[0.9791935,0.0001370262,0.01899991,0.0002003373,0.00007853765,0.00008839038,0.001107553,0.00001508799,0.0001795483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004023909,"threshold_uncertainty_score":0.02128071,"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."}}