{"id":"W3094027353","doi":"10.2196/20324","title":"Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"RIKEN","keywords":"Medicine; Predictive modelling; Computer science; Medical emergency; Machine learning","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":[0.0003415854,0.0002997431,0.0004777471,0.0001050128,0.0003458617,0.00003278786,0.0002006677,0.0001592966,0.00009965736],"category_scores_gemma":[0.0001957203,0.0002365207,0.0001244826,0.0002800098,0.00005803673,0.000366851,0.0002170104,0.0005502326,0.00002724972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001903876,"about_ca_system_score_gemma":0.0006412959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002324707,"about_ca_topic_score_gemma":0.000002136908,"domain_scores_codex":[0.9967656,0.0000205437,0.00109928,0.0002591141,0.001451541,0.0004039609],"domain_scores_gemma":[0.9983405,0.00003602304,0.0001359037,0.0001949914,0.0002654033,0.001027216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001743224,0.006510807,0.09851263,0.003214568,0.001742983,0.00009096123,0.4816797,0.08339468,0.00005347321,0.002349125,0.1034411,0.2172667],"study_design_scores_gemma":[0.004277025,0.004628149,0.01439149,0.0002812323,0.00009552592,0.000008400514,0.01307498,0.9127175,0.00003352484,0.00003284059,0.05009549,0.0003638986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6405891,0.0001523092,0.3435856,0.003438886,0.0006113905,0.004717107,0.00006308701,0.0005155711,0.006326946],"genre_scores_gemma":[0.9768603,0.0000910243,0.02069212,0.0007737352,0.0003455553,0.0008985137,0.0001894673,0.00002822354,0.0001210545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8293228,"threshold_uncertainty_score":0.9645036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05026349916723954,"score_gpt":0.32132486006233,"score_spread":0.2710613608950905,"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."}}