{"id":"W4229378776","doi":"10.1016/j.rmed.2022.106866","title":"Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model","year":2022,"lang":"en","type":"article","venue":"Respiratory Medicine","topic":"Asthma and respiratory diseases","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hôpital du Sacré-Cœur de Montréal; Université de Montréal; McGill University","funders":"Canadian Institutes of Health Research; Teva Pharmaceutical Industries; GlaxoSmithKline","keywords":"Medicine; Asthma; Exacerbation; Cohort; Logistic regression; Asthma exacerbations; Medical record; Odds; Odds ratio; Emergency medicine; Retrospective cohort study; Ambulatory; Intensive care medicine; Internal medicine; Pediatrics","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":[0.001720576,0.0007170537,0.001289628,0.001525739,0.0004855819,0.001495618,0.00129207,0.0015456,0.001988381],"category_scores_gemma":[0.005727981,0.000287685,0.001170016,0.0007250697,0.0003263023,0.001049814,0.0004908635,0.001686665,0.0005363761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006261062,"about_ca_system_score_gemma":0.0008201826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058668,"about_ca_topic_score_gemma":0.008338132,"domain_scores_codex":[0.9995071,0.0001673272,0.00004422989,0.0001506254,0.00005709244,0.00007373529],"domain_scores_gemma":[0.9962579,0.003024187,0.0002258308,0.00009692227,0.0002805151,0.0001146129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001731987,0.003119881,0.3402178,0.0001625154,0.0008918445,0.0006866227,0.0002228228,0.4231865,0.001560504,0.002801601,0.00764203,0.217776],"study_design_scores_gemma":[0.00002877865,0.0001102229,0.01064244,0.00001760515,0.00007803222,0.000102978,0.00002516902,0.987267,0.0001012762,0.001437017,0.0001765837,0.00001286592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8832413,0.002166712,0.1030647,0.004790702,0.0003137265,0.0001626712,0.001476081,0.000599571,0.004184493],"genre_scores_gemma":[0.9871946,0.0003478392,0.009628695,0.0002498751,0.0001487528,0.00006191613,0.0007729149,0.00001688002,0.001578466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01058668,"threshold_uncertainty_score":0.0210501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140432213871695,"score_gpt":0.2791581447215616,"score_spread":0.2577538225828446,"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."}}