{"id":"W4411292988","doi":"10.2337/db25-964-p","title":"964-P: Reducing Discordance between GMI and A1C Using AI","year":2025,"lang":"en","type":"article","venue":"Diabetes","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004130194,0.00007834381,0.0001041563,0.00005816676,0.00005830373,0.00005543627,0.00004766196,0.00003275146,0.000006602256],"category_scores_gemma":[0.00001322525,0.00007822838,0.00001268399,0.00009271631,0.00001520583,0.0001192012,0.00002383858,0.0000688796,0.00000106099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001649859,"about_ca_system_score_gemma":0.00000581458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007919982,"about_ca_topic_score_gemma":5.296333e-7,"domain_scores_codex":[0.9996189,0.000004590281,0.000093549,0.000105311,0.00003716748,0.000140492],"domain_scores_gemma":[0.9998447,0.00002694078,0.0000112724,0.00008335492,0.0000106116,0.00002307802],"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":[0.000001637584,0.00001074456,0.2124519,0.001397986,0.0001216294,9.383072e-7,0.0004280444,0.695364,0.01053006,0.000542615,0.0005412717,0.07860921],"study_design_scores_gemma":[0.0002905809,0.0000121517,0.06387831,0.0005454432,0.00007290184,1.612677e-7,0.00003505931,0.5880725,0.3429413,0.002166817,0.001605691,0.0003790363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831259,0.0008845128,0.01487334,0.00006572356,0.0001317998,0.00005024425,0.000003847839,0.0001264017,0.000738248],"genre_scores_gemma":[0.9976187,0.00003187882,0.002139693,0.00004210795,0.00005325147,0.000004201244,0.000005998118,0.00001194929,0.0000922002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3324113,"threshold_uncertainty_score":0.3190061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007760045532330505,"score_gpt":0.2263964892542341,"score_spread":0.2186364437219036,"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."}}