{"id":"W4406863677","doi":"10.2196/66831","title":"Applications of AI in Predicting Drug Responses for Type 2 Diabetes","year":2025,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novo Nordisk","keywords":"Preprint; Artificial intelligence; Drug; Computer science; Medicine; Pharmacology; World Wide Web","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":[0.003196631,0.001055586,0.0008133128,0.002268341,0.0004050479,0.001290461,0.0007783263,0.0009638572,0.001646384],"category_scores_gemma":[0.01420422,0.0001964833,0.0006782137,0.001793698,0.0003432975,0.0006958283,0.000713214,0.001575924,0.0004421274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008610574,"about_ca_system_score_gemma":0.0008652206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006803042,"about_ca_topic_score_gemma":0.004027502,"domain_scores_codex":[0.9990036,0.0004696077,0.00009873967,0.0001588848,0.0002199549,0.00004923102],"domain_scores_gemma":[0.9907159,0.0073782,0.0006380454,0.0002688615,0.0007843233,0.0002147805],"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":[0.0004209433,0.000317139,0.07068489,0.0003546378,0.0005750636,0.0001561738,0.0001277917,0.5361441,0.001143796,0.002951376,0.005181252,0.3819428],"study_design_scores_gemma":[0.00001217671,0.0001079066,0.005738636,0.00005716589,0.00006203861,0.00008760762,0.00002991925,0.9864317,0.0005775128,0.005271663,0.001605553,0.0000180784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.289606,0.0288131,0.6481532,0.009574913,0.001007737,0.0003737708,0.003085985,0.003075917,0.01630929],"genre_scores_gemma":[0.9111038,0.003684455,0.08108915,0.0008085549,0.0005161886,0.0001479955,0.001173048,0.00004941284,0.00142728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006803042,"threshold_uncertainty_score":0.01690561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314678531705251,"score_gpt":0.3451500233932272,"score_spread":0.3320032380761747,"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."}}