{"id":"W4410942890","doi":"10.2196/67748","title":"Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Medicine; Diabetes mellitus; Machine learning; Area under the curve; Artificial intelligence; Medical record; F1 score; Internal medicine; Computer science","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":[0.002018618,0.0009003149,0.0006515128,0.0009323978,0.0002465173,0.0008137821,0.001008529,0.0006021529,0.001592109],"category_scores_gemma":[0.006761404,0.0003560148,0.0007856157,0.000572546,0.0002430407,0.0007941088,0.0006550677,0.001217839,0.0004909657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030734,"about_ca_system_score_gemma":0.001533372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01216299,"about_ca_topic_score_gemma":0.009124909,"domain_scores_codex":[0.9995652,0.0001986995,0.00003494953,0.00009786782,0.00006544501,0.00003792698],"domain_scores_gemma":[0.9967476,0.00251034,0.0001517492,0.0001044756,0.0004229606,0.00006281553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002308169,0.0001570046,0.01379536,0.00007154936,0.00009710615,0.00009501123,0.00004718195,0.8794012,0.0008627807,0.00149748,0.001488904,0.1022556],"study_design_scores_gemma":[0.00000593555,0.00001848886,0.0001880348,0.000003245884,0.000006729709,0.000008179763,0.000003704304,0.9988075,0.0002509278,0.0005824303,0.0001223959,0.000002296099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2036393,0.001233169,0.7847801,0.001263006,0.0001117844,0.0002977388,0.001079541,0.003889751,0.003705716],"genre_scores_gemma":[0.8670735,0.0004403034,0.1292452,0.0002459226,0.00004706294,0.0002057908,0.001081487,0.00007022751,0.001590627],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01216299,"threshold_uncertainty_score":0.02418435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420582543821531,"score_gpt":0.3072878683713109,"score_spread":0.2930820429330956,"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."}}