{"id":"W4412019798","doi":"10.2196/72874","title":"Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework","year":2025,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Diabetes Management and Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glycemic; Reinforcement learning; Hypoglycemia; Computer science; Diabetes management; Reinforcement; Diabetes treatment; Artificial intelligence; Machine learning; Medicine; Type 2 diabetes; Diabetes mellitus; Engineering","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":[0.003993975,0.0006479717,0.0007097492,0.0003391094,0.0003426252,0.0007815366,0.001246906,0.0008844036,0.0009240012],"category_scores_gemma":[0.006690645,0.0002741273,0.000556174,0.0001935211,0.000822774,0.0006327137,0.001182274,0.001518718,0.0001204319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001291085,"about_ca_system_score_gemma":0.002495765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007999633,"about_ca_topic_score_gemma":0.004633281,"domain_scores_codex":[0.9989453,0.0005089598,0.00004814403,0.0001842187,0.0001911083,0.000122328],"domain_scores_gemma":[0.996011,0.002666736,0.0003134053,0.0002018809,0.000593644,0.0002132702],"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":[0.00004885011,0.0000611135,0.0007305666,0.00001794085,0.00001687281,0.00003158075,0.00002314094,0.9877976,0.0003541874,0.001410573,0.000118427,0.009389279],"study_design_scores_gemma":[0.000006945102,0.0000253379,0.00005900269,0.000002456294,0.000002959391,0.000003978549,0.000002382071,0.9991906,0.0001766547,0.0004699425,0.0000581173,0.000001577457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1025138,0.0003667085,0.8930748,0.0005947098,0.00005208116,0.000168825,0.00008353684,0.0006790037,0.002466554],"genre_scores_gemma":[0.9025974,0.0001154972,0.09613648,0.0001516265,0.00001625723,0.0001289217,0.00005860482,0.0000271876,0.0007681521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007999633,"threshold_uncertainty_score":0.0211224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673917541191849,"score_gpt":0.3088159497178257,"score_spread":0.2920767743059072,"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."}}