{"id":"W4407392303","doi":"10.2147/dmso.s480317","title":"Support Vector Machine for Stratification of Cognitive Impairment Using 3D T1WI in Patients with Type 2 Diabetes Mellitus","year":2025,"lang":"en","type":"article","venue":"Diabetes Metabolic Syndrome and Obesity","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Commission of Shanxi Province","keywords":"Stratification (seeds); Cognitive impairment; Diabetes mellitus; Risk stratification; Type 2 Diabetes Mellitus; Medicine; Support vector machine; Internal medicine; Cognition; Computer science; Artificial intelligence; Endocrinology; Psychiatry; Biology","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":[0.001562738,0.000629306,0.0005460154,0.001619471,0.0001754164,0.0006740351,0.0003698073,0.0004353327,0.0007231692],"category_scores_gemma":[0.004263565,0.0001348372,0.0005284993,0.0005845331,0.0001486433,0.0004574583,0.0003329485,0.0004580624,0.000250339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001908778,"about_ca_system_score_gemma":0.0002694478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001709187,"about_ca_topic_score_gemma":0.001244874,"domain_scores_codex":[0.999513,0.0002027761,0.00005720216,0.0001016996,0.00008163926,0.00004362527],"domain_scores_gemma":[0.9990516,0.0004268324,0.0001822934,0.00006856865,0.0001902809,0.00008028339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001463356,0.0002766001,0.9051964,0.00007504204,0.0002398702,0.0001887635,0.00009050201,0.007303912,0.00133696,0.00008681633,0.0009926951,0.08274905],"study_design_scores_gemma":[0.0000906649,0.001152374,0.4306877,0.0001200776,0.0002843638,0.0007454574,0.0004006814,0.5624833,0.001968406,0.001010745,0.001001903,0.00005448198],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886152,0.001076043,0.00895103,0.0001988962,0.00005243459,0.00003779071,0.0005575087,0.00008706385,0.0004240522],"genre_scores_gemma":[0.9961374,0.0001956773,0.003016667,0.00002139858,0.00002559358,0.00002403396,0.0004323059,0.000003677799,0.0001432223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001709187,"threshold_uncertainty_score":0.008264661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00681001900251557,"score_gpt":0.2576377520362524,"score_spread":0.2508277330337369,"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."}}