{"id":"W4407285986","doi":"10.2196/58107","title":"Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Algorithm; Medicine; Diabetic retinopathy; Dyslipidemia; Cohort; Machine learning; Receiver operating characteristic; Type 2 Diabetes Mellitus; Medical record; Kidney disease; Artificial intelligence; Diabetes mellitus; Internal medicine; Disease; Computer science","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":[0.008800259,0.0008790251,0.0008730885,0.001049134,0.0003765788,0.000925447,0.001019574,0.001174786,0.0007970167],"category_scores_gemma":[0.01811883,0.000281153,0.0008113265,0.0006603795,0.0002680785,0.0005993987,0.0007928945,0.001087762,0.0002639294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007403687,"about_ca_system_score_gemma":0.001945003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446669,"about_ca_topic_score_gemma":0.002778661,"domain_scores_codex":[0.9978023,0.001283168,0.0002077794,0.0003056199,0.0002773447,0.0001236698],"domain_scores_gemma":[0.9890847,0.007647814,0.000394969,0.0004291416,0.002263367,0.0001800746],"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":[0.001533908,0.002045418,0.1686226,0.0003241323,0.0008729981,0.0002460209,0.0001846207,0.3493017,0.002940791,0.0008650668,0.004790828,0.468272],"study_design_scores_gemma":[0.0002045217,0.0004357835,0.007964587,0.0000438068,0.00009006822,0.0000997107,0.0000376167,0.9887779,0.001569833,0.0002420659,0.0005234202,0.00001069353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7717709,0.001860434,0.2200961,0.0007019055,0.0001421934,0.00121445,0.0005711589,0.00156704,0.002075708],"genre_scores_gemma":[0.7592867,0.0004515062,0.2369066,0.0002395954,0.00003649355,0.0008372333,0.00138214,0.00006520227,0.0007945819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008800259,"threshold_uncertainty_score":0.04654074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007827748059360163,"score_gpt":0.2661508894461005,"score_spread":0.2583231413867403,"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."}}