{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01003418,0.001130631,0.0009926453,0.001142772,0.0006168334,0.002037207,0.002214045,0.0008411811,0.004376378],"category_scores_gemma":[0.03808333,0.0003568056,0.001420036,0.0009326475,0.0006467412,0.001156399,0.001406216,0.001507929,0.001498541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00149828,"about_ca_system_score_gemma":0.002474949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01615555,"about_ca_topic_score_gemma":0.006761163,"domain_scores_codex":[0.9955508,0.002315369,0.0002677798,0.001069009,0.0006124627,0.0001846267],"domain_scores_gemma":[0.9875757,0.008669935,0.001309939,0.0009830468,0.001160385,0.0003010228],"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":[0.003555622,0.0008734897,0.4187611,0.0004928961,0.001733148,0.0002236954,0.0009532637,0.1259307,0.002500107,0.003341574,0.0114755,0.430159],"study_design_scores_gemma":[0.0003678383,0.001644906,0.1247838,0.0002923995,0.0007799136,0.0005552254,0.0005899641,0.8476919,0.005349232,0.009552239,0.008261982,0.0001305601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6631288,0.001925333,0.3094735,0.003752434,0.0003149044,0.0007431815,0.003438257,0.003398667,0.01382476],"genre_scores_gemma":[0.9253342,0.000232059,0.06950814,0.0004264616,0.00009388183,0.0002944415,0.001912409,0.0001609514,0.002037504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01615555,"threshold_uncertainty_score":0.05306649,"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."}}