{"id":"W4412587150","doi":"10.2196/72616","title":"Artificial Intelligence in Diabetic Kidney Disease Research: Bibliometric Analysis From 2006 to 2024","year":2025,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Chronic Kidney Disease and Diabetes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Medicine; Computer science; 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":["metaepi_narrow","bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0007810073,0.0002910949,0.0007060061,0.09283773,0.0001185062,0.000174746,0.0003778246,0.0001390999,0.002217674],"category_scores_gemma":[0.004528636,0.0002734521,0.000347443,0.2344882,0.0001864607,0.0001443358,0.0002970635,0.0004588439,0.0007281819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003288461,"about_ca_system_score_gemma":0.0009697487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002177209,"about_ca_topic_score_gemma":0.00005830009,"domain_scores_codex":[0.99638,0.0002052929,0.0006597503,0.0008867497,0.0008247161,0.001043487],"domain_scores_gemma":[0.9960341,0.0009976255,0.00005267298,0.0009758126,0.0002805574,0.001659185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001372575,0.0009431082,0.9032652,0.0002618274,0.0005422244,0.00004028723,0.00008894686,0.00006579839,0.001456185,0.0003497631,0.05313139,0.03971797],"study_design_scores_gemma":[0.0003404333,0.000133625,0.9647399,0.0007086431,0.000998866,1.778939e-8,0.0001430201,0.004464257,0.006289644,0.01337256,0.008472231,0.0003368183],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840423,0.004481819,0.00008826183,0.006575311,0.0002696984,0.0011427,0.0005019094,0.00009750389,0.002800464],"genre_scores_gemma":[0.9934042,0.00008728171,0.0001874977,0.003434472,0.0002373077,0.0006251828,0.0003598241,0.00002833663,0.001635876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1416505,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04888532462776628,"score_gpt":0.3829506560281897,"score_spread":0.3340653314004234,"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."}}