{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312499,0.00009724926,0.0001930585,0.0002906545,0.00008592093,0.0000410192,0.0006112521,0.00005561464,0.000003091733],"category_scores_gemma":[0.000597646,0.00009781874,0.00004448036,0.0009026958,0.00003910892,0.0001466688,0.0001770845,0.0001886564,0.000003876786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003624125,"about_ca_system_score_gemma":0.0001505041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001973817,"about_ca_topic_score_gemma":0.00001781882,"domain_scores_codex":[0.9988731,0.0001238341,0.0003031985,0.0002971912,0.0001144321,0.000288283],"domain_scores_gemma":[0.9975991,0.00163112,0.00009723382,0.0004758037,0.0001598515,0.00003689207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004997373,0.00004744258,0.9422496,0.0004479125,0.0000103483,7.880387e-8,0.0003669746,0.00007448599,0.0008571983,0.01399654,0.001229066,0.04071536],"study_design_scores_gemma":[0.0007220445,0.000218031,0.6945675,0.0006307057,0.00001342095,9.063684e-8,0.00008930338,0.1978553,0.01539656,0.03415336,0.05600915,0.0003444899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825054,0.001026941,0.006995304,0.007146381,0.0002473707,0.001289696,0.0000127556,0.0002153458,0.0005608422],"genre_scores_gemma":[0.989148,0.000004232341,0.008803537,0.0006161673,0.00003919031,0.0008602139,0.000006331853,0.00000848082,0.0005138874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2476821,"threshold_uncertainty_score":0.3988932,"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."}}