{"id":"W4416685526","doi":"10.1016/j.jcjd.2025.10.038","title":"Using Glucose Sensor Technology to Predict Future Risk of Diabetes","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Diabetes Management and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Diabetes mellitus; Continuous glucose monitoring; Risk assessment; Diabetes treatment","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":[0.0005211629,0.0005488864,0.0003268549,0.001289992,0.0001869573,0.001302412,0.0003118297,0.0006160943,0.001054083],"category_scores_gemma":[0.002449077,0.000241906,0.0003458241,0.0008738417,0.00009370863,0.0004321047,0.0002130443,0.0006868737,0.0004730449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003324198,"about_ca_system_score_gemma":0.0003525559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01374825,"about_ca_topic_score_gemma":0.02370333,"domain_scores_codex":[0.999698,0.00008165632,0.00002457817,0.00004512571,0.000120851,0.00002985875],"domain_scores_gemma":[0.9992274,0.000379849,0.0001450423,0.000035548,0.0001437214,0.00006836914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001958927,0.0001391941,0.9704676,0.00002766628,0.0001164403,0.0000885446,0.00003435227,0.001555267,0.000657466,0.0001323101,0.0009317104,0.02565359],"study_design_scores_gemma":[0.00003985412,0.0006905493,0.8986305,0.00009559905,0.0004316639,0.0007802958,0.0002823318,0.08963849,0.003251495,0.00171218,0.004387417,0.00005952433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702781,0.002955286,0.009744663,0.0009220074,0.0003365745,0.00005000257,0.00373027,0.0002623786,0.01172084],"genre_scores_gemma":[0.9917381,0.001185819,0.004608253,0.0001097178,0.00008253818,0.00001441957,0.001032464,0.00001018686,0.001218574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01374825,"threshold_uncertainty_score":0.02733648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126940893669689,"score_gpt":0.2757559406946435,"score_spread":0.2644865317579466,"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."}}