{"id":"W4387914853","doi":"10.2196/49886","title":"A Large Language Model Screening Tool to Target Patients for Best Practice Alerts: Development and Validation","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Best practice; Medical prescription; Deep vein; Health care; Medical emergency; Population; Diagnosis code; Intensive care medicine; Emergency department; Emergency medicine; Thrombosis; Surgery; Nursing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00710561,0.001805215,0.0005455124,0.001318888,0.0003686706,0.000939298,0.00222246,0.001437938,0.003734985],"category_scores_gemma":[0.03096763,0.0006512487,0.0009491,0.0005456398,0.0004605677,0.001006478,0.00142257,0.002276765,0.001393786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001311725,"about_ca_system_score_gemma":0.002502905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127585,"about_ca_topic_score_gemma":0.0110754,"domain_scores_codex":[0.9975134,0.001261473,0.0002301436,0.0004438979,0.0004173488,0.0001336996],"domain_scores_gemma":[0.9820012,0.01375428,0.0004523332,0.0009436255,0.002475799,0.0003727037],"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":[0.001851263,0.004780487,0.05220024,0.0009119243,0.00082228,0.0009151954,0.0007956931,0.409559,0.01355666,0.002443146,0.03349868,0.4786656],"study_design_scores_gemma":[0.0002919792,0.000460641,0.003875198,0.00005928506,0.00008015824,0.0001250218,0.00009020566,0.9854595,0.00681067,0.0007218832,0.00199383,0.00003168136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6126804,0.0008600983,0.3360742,0.002198641,0.0003741527,0.003448508,0.00691528,0.03251661,0.004932057],"genre_scores_gemma":[0.6708543,0.0002818514,0.3124062,0.0008807961,0.00005114161,0.001921991,0.01084782,0.0004961673,0.002259743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0127585,"threshold_uncertainty_score":0.03757846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02960621836339888,"score_gpt":0.3537486169459002,"score_spread":0.3241423985825013,"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."}}