{"id":"W4406392207","doi":"10.1007/s11255-025-04363-y","title":"Current applications and research trends of ultrasound examination in acute kidney injury assessment: a bibliometric analysis","year":2025,"lang":"en","type":"article","venue":"International Urology and Nephrology","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tianjin Science and Technology Program","keywords":"Medicine; Nephrology; Acute kidney injury; Ultrasound; Intensive care medicine; Internal medicine; Medical physics; 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":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.001365656,0.000104909,0.0003386955,0.08910988,0.00006964038,0.00001645484,0.0001857139,0.0002019286,0.0002762212],"category_scores_gemma":[0.0004563493,0.0001004934,0.00005295814,0.04588467,0.0005832289,0.00009300179,0.0001865781,0.0005922447,0.000003278363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007469171,"about_ca_system_score_gemma":0.0002092536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004464817,"about_ca_topic_score_gemma":0.00001208405,"domain_scores_codex":[0.998323,0.0003322427,0.0003671144,0.0004018398,0.0003186266,0.0002572251],"domain_scores_gemma":[0.9984092,0.0007468132,0.0000793099,0.0002143211,0.0004235968,0.0001268164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006175229,0.0004770376,0.8618648,0.0000388058,0.001635465,0.00001282971,0.00008249519,7.031015e-7,0.008584231,0.02296675,0.008314135,0.09540524],"study_design_scores_gemma":[0.001138714,0.0003736588,0.9439086,0.000006040507,0.0003498383,0.00003553251,0.00001183426,0.001093266,0.000174112,0.002456398,0.05039974,0.00005224627],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694059,0.0008785238,0.003025252,0.01649173,0.0001319881,0.0003910261,0.0003490388,0.00002039984,0.009306099],"genre_scores_gemma":[0.995315,0.002375365,0.0002699401,0.0009381105,0.00004289753,0.0002536245,0.0003542398,0.000005758304,0.0004451029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09535299,"threshold_uncertainty_score":0.9743955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969114916870612,"score_gpt":0.4447244874292697,"score_spread":0.4150333382605636,"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."}}