{"id":"W4409638536","doi":"10.1016/j.ijmedinf.2025.105942","title":"Ontology accelerates few-shot learning capability of large language model: A study in extraction of drug efficacy in a rare pediatric epilepsy","year":2025,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University; National Institutes of Health; Eisai; Dravet Syndrome Foundation; National Institute on Aging; Patient-Centered Outcomes Research Institute; National Institute on Drug Abuse; Epilepsy Foundation; U.S. Department of Defense","keywords":"Ontology; Epilepsy; Computer science; Antiepileptic drug; Drug; One shot; Natural language processing; Shot (pellet); Extraction (chemistry); Artificial intelligence; Information extraction; Machine learning; Medicine; Pharmacology","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":[0.001964286,0.00100219,0.0007141046,0.002460769,0.0005285403,0.0007439918,0.0008927014,0.001034278,0.001047051],"category_scores_gemma":[0.006491667,0.0002075423,0.001453132,0.001583693,0.0003969693,0.001723262,0.0008144257,0.001054886,0.0003435552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009551998,"about_ca_system_score_gemma":0.001691995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009276121,"about_ca_topic_score_gemma":0.01205535,"domain_scores_codex":[0.9988738,0.0003429952,0.0001372871,0.0003622616,0.000196826,0.00008683935],"domain_scores_gemma":[0.9959727,0.003125931,0.0002022161,0.0002324906,0.0003413642,0.0001253559],"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":[0.001665893,0.00153877,0.03819348,0.001661862,0.0007909343,0.001644587,0.0004758581,0.1351281,0.01731184,0.00362968,0.02031958,0.7776395],"study_design_scores_gemma":[0.0001299535,0.0004429746,0.008534415,0.00006798083,0.0003198675,0.0003963546,0.0002683796,0.9676983,0.01040294,0.004258833,0.007429052,0.00005098457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7219061,0.01191758,0.2435016,0.002169182,0.0004647595,0.0005092115,0.008696636,0.006449043,0.004385853],"genre_scores_gemma":[0.8191145,0.001926213,0.1577061,0.0006363371,0.0001811364,0.0002405822,0.01817272,0.000190807,0.001831582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009276121,"threshold_uncertainty_score":0.01844424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02432106158331184,"score_gpt":0.3675098786225851,"score_spread":0.3431888170392733,"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."}}