{"id":"W4402575004","doi":"10.1093/ckj/sfae286","title":"Cerebral white matter injury in haemodialysis patients: a cross-sectional tract-based spatial statistics and fixel-based analysis","year":2024,"lang":"en","type":"article","venue":"Clinical Kidney Journal","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Friendship Hospital, Capital Medical University; Beijing Municipal Administration of Hospitals; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support; Capital Medical University; National Natural Science Foundation of China","keywords":"White matter; Cross-sectional study; Hemodialysis; Medicine; Statistics; Internal medicine; Magnetic resonance imaging; Pathology; Mathematics; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001441279,0.0002594799,0.0007452662,0.0009961332,0.0001274079,0.0005207882,0.0001287591,0.000191645,0.00461989],"category_scores_gemma":[0.0008889617,0.0001976561,0.0008700705,0.0008532875,0.0002458611,0.0001409895,0.00005309292,0.0008294882,0.0001208894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467085,"about_ca_system_score_gemma":0.0005782605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003409001,"about_ca_topic_score_gemma":0.00001874707,"domain_scores_codex":[0.9965133,0.0003118428,0.00154745,0.0005562452,0.0007088984,0.0003622565],"domain_scores_gemma":[0.9978726,0.000331237,0.0002397782,0.000271185,0.0002526493,0.001032576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006363066,0.001277119,0.971437,0.0001526568,0.001123961,0.0002404871,0.00002338517,0.0005185789,0.000002040412,0.00001513349,0.01811384,0.006459456],"study_design_scores_gemma":[0.003055196,0.0003768713,0.9367101,0.0001543489,0.002707803,0.000004474054,0.000005290451,0.05219639,0.000003279803,0.0001450257,0.004434019,0.000207189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9382739,0.00009769181,0.05494064,0.003706778,0.001179253,0.0003127281,0.0008791968,0.00004306193,0.0005667686],"genre_scores_gemma":[0.9893855,0.00002961804,0.002350054,0.006459132,0.0006825358,0.00001314409,0.0006016772,0.00003255313,0.000445816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05259059,"threshold_uncertainty_score":0.99629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0217254456256383,"score_gpt":0.3560792096484537,"score_spread":0.3343537640228154,"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."}}