{"id":"W2005142741","doi":"10.1007/s10916-009-9268-7","title":"A Tree-Based Decision Model to Support Prediction of the Severity of Asthma Exacerbations in Children","year":2009,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Ottawa; California HIV/AIDS Research Program","keywords":"Triage; Medicine; Emergency department; Asthma; Brier score; Health informatics; Asthma exacerbations; Decision tree; Guideline; Clinical Practice; Medical record; Data mining; Emergency medicine; Machine learning; Computer science; Internal medicine; Physical therapy","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":[0.001190978,0.00007604649,0.0004024743,0.0001339647,0.00002638596,0.000002191633,0.0001656932,0.000106902,0.0000301832],"category_scores_gemma":[0.0008903533,0.00004372058,0.0001602702,0.0002990175,0.00004048427,0.00004426882,0.00002070858,0.0002453464,5.014154e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005635514,"about_ca_system_score_gemma":0.0003155347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004444437,"about_ca_topic_score_gemma":0.00001331264,"domain_scores_codex":[0.9975218,0.00006873898,0.0009004883,0.00007743813,0.001326305,0.0001052342],"domain_scores_gemma":[0.9990672,0.00006375747,0.0003024005,0.0001442444,0.0002739075,0.0001485313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00123911,0.002218694,0.7903988,0.0004124266,0.000388574,0.00005120378,0.002412701,0.01268499,0.004291059,0.0004040391,0.1096891,0.07580926],"study_design_scores_gemma":[0.003453984,0.001875338,0.961048,0.003574471,0.0001215341,0.0002760671,0.000388846,0.027779,0.0007981288,0.0001213064,0.0004882375,0.0000750585],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852555,0.0005210291,0.009936247,0.003257621,0.0003960677,0.0002905197,0.00002387167,0.000004063749,0.0003150651],"genre_scores_gemma":[0.999256,0.0001177684,0.0002388054,0.0001698267,0.0001784926,0.000002075495,0.000001849867,0.000003942484,0.00003122055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1706492,"threshold_uncertainty_score":0.1782874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969736355246104,"score_gpt":0.3002268292009654,"score_spread":0.2805294656485044,"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."}}