{"id":"W4405507526","doi":"10.21203/rs.3.rs-5453999/v1","title":"MIRACLE - Medical Information Retrieval using Clinical Language Embeddings for Retrieval Augmented Generation at the point of care","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Miracle; Point (geometry); Information retrieval; Medical information; Point of care; Computer science; Natural language processing; Medicine; Mathematics; Political science; Nursing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002658412,0.001462681,0.00140214,0.001963095,0.0007191542,0.001980746,0.001678727,0.002539906,0.01248193],"category_scores_gemma":[0.008452442,0.0006885008,0.001969664,0.001084027,0.0005073734,0.002247788,0.002745484,0.002004291,0.01021727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007612685,"about_ca_system_score_gemma":0.002167836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004786439,"about_ca_topic_score_gemma":0.007369574,"domain_scores_codex":[0.9980672,0.0008403545,0.0001000539,0.0004810903,0.0003221914,0.0001890784],"domain_scores_gemma":[0.997499,0.001172776,0.00009698675,0.0005801076,0.0005176227,0.0001334597],"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":[0.001689819,0.0007148604,0.002583752,0.000766173,0.0004112032,0.0003314535,0.0002480549,0.05112091,0.0177684,0.01124337,0.1257026,0.7874194],"study_design_scores_gemma":[0.0003431835,0.0006071716,0.001323261,0.00009656129,0.0001735711,0.0005171228,0.0001212764,0.933908,0.01665267,0.01913877,0.0270253,0.00009317139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03148861,0.00470929,0.9059762,0.003497039,0.001648494,0.0007546514,0.01131073,0.03190937,0.008705627],"genre_scores_gemma":[0.3180728,0.001613066,0.6020691,0.001696676,0.001416681,0.0009997003,0.04009326,0.002156016,0.03188267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01248193,"threshold_uncertainty_score":0.04175621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254270944562968,"score_gpt":0.4730842611698082,"score_spread":0.3476571667135114,"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."}}