{"id":"W4400863368","doi":"10.2196/57035","title":"Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health records; Computer science; Quality (philosophy); Data science; Triage; Health care; Data quality; Data extraction; Process (computing); Data mining; Medicine; MEDLINE; Metric (unit); Engineering; Medical emergency","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":[0.002828806,0.0001038434,0.0002712475,0.0001531123,0.00006263116,0.00005136417,0.0003597776,0.0001464886,0.000003409556],"category_scores_gemma":[0.0002072194,0.00009221276,0.00001587265,0.0001018435,0.00005517006,0.0008526841,0.0003531406,0.0004768769,3.797783e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006164432,"about_ca_system_score_gemma":0.00193602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001664893,"about_ca_topic_score_gemma":0.00005476818,"domain_scores_codex":[0.9978928,0.0001220309,0.001157245,0.0002053599,0.0003723658,0.0002501741],"domain_scores_gemma":[0.998838,0.0004033296,0.0002087203,0.0002985334,0.00006964299,0.0001817888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006095975,0.00001287807,0.0001890709,0.005227213,0.00001269235,0.000001001389,0.03153377,0.00001226744,2.597955e-8,0.006341191,0.0004063389,0.9562575],"study_design_scores_gemma":[0.0003540087,0.000308058,0.0009943811,0.0007326392,0.000002867633,0.00001401499,0.005330246,0.9255962,0.000003574214,0.0009421739,0.0656276,0.00009427294],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4032977,0.05784801,0.4735903,0.05674725,0.001877899,0.004027201,0.0003251305,0.0008813152,0.001405105],"genre_scores_gemma":[0.8045167,0.0326646,0.1591854,0.001912848,0.0002352939,0.0002208381,0.001203976,0.00003171557,0.00002857544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9561632,"threshold_uncertainty_score":0.3760327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1490105410860406,"score_gpt":0.3951361375382488,"score_spread":0.2461255964522083,"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."}}